Title: Discrimination of an invasive plant, Schinus terebinthifolius, from aerial digital imagery
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 Material Information
Title: Discrimination of an invasive plant, Schinus terebinthifolius, from aerial digital imagery
Physical Description: Book
Language: English
Creator: Pearlstine, Leonard G
Publisher: State University System of Florida
Place of Publication: Florida
Florida
Publication Date: 2000
Copyright Date: 2000
 Subjects
Subject: Civil and Coastal Engineering thesis, Ph. D   ( lcsh )
Dissertations, Academic -- Civil and Coastal Engineering -- UF   ( lcsh )
Genre: government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )
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Summary: ABSTRACT: Schinus terebinthifolius, known as Brazilian pepper, is an exotic, invasive plant species in Florida that displaces native plant species to the detriment of beneficial native plant and animal habitat in aquatic, wetland, and terrestrial environments. Rapidly and accurately identifying invasive species is critical to successful monitoring and management of ecosystem change. Aerial surveys typically are used for this purpose, but observers or photointerpretation may be inconsistent and costly. Image processing for the classification of land cover types that relies on by-pixel spectral response of the different vegetation in an image to discriminate the cover types is problematic. In high-resolution imagery, adjacent pixels of the same cover type are likely to represent different parts of the plant with very different spectral responses. Measuring spatial patterns of texture in neighborhoods of pixels can be an effective alternative. Textures derived from first-order statistics, co-occurrence matrices, and components of edge in high-resolution digital color infrared images were tested for their application to the discrimination of Schinus terebinthifolius. Logistic regression on the texture variables found the combination of the mean, Sobel edge density, Sobel edge magnitude, and edge standard deviation to be the best discriminators of Schinus in these images with a median user's accuracy of 87.3 percent and producer's accuracy of 44.6 percent. The high user's accuracy and lower producer's accuracy reflect the model's consistent identification of core areas of Schinus, but not the edges of Schinus patches.
Summary: ABSTRACT (cont.): Misidentification of other cover types as Schinus was extremely low except where Sabal palmetto was present in the images. Contextual discrimination between Schinus and Sabal requires more exploration. Logistic regressions also were used for combined classifications of all cover types in an image. User accuracies for Schinus generally remained high, but omission errors increased in cases where Schinus was misidentified as one of the other cover types.
Summary: KEYWORDS: Brazilian pepper, Schinus terebinthifolius, image processing, texture analysis, edge detection, aerial imagery
Thesis: Thesis (Ph. D.)--University of Florida, 2000.
Bibliography: Includes bibliographical references (p. 283-287).
System Details: System requirements: World Wide Web browser and PDF reader.
System Details: Mode of access: World Wide Web.
Statement of Responsibility: by Leonard G. Pearlstine.
General Note: Title from first page of PDF file.
General Note: Document formatted into pages; contains xvi, 289 p.; also contains graphics.
General Note: Vita.
 Record Information
Bibliographic ID: UF00100815
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 47682228
alephbibnum - 002678737
notis - ANE5964

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DISCRIMINATION OF AN INVASIVE PLANT, Schinus terebinthifolius, FROM
AERIAL DIGITAL IMAGERY















By

LEONARD G. PEARLSTINE


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2000



























DISCRIMINATION OF AN INVASIVE PLANT, Schinus terebinthifolius, FROM
AERIAL DIGITAL IMAGERY

Copyright 2000

by

Leonard G. Pearlstine















ACKNOWLEDGMENTS

I owe a special debt to my parents for their caring support and the time they have

shared with my children. They were valuable partners in completing this dissertation. I

also thank my children, Timothy and Benjamin, for their patience with me during absent

evenings and weekends. We have some fun to catch up on.

The members of my committee--Scot Smith (chair), Bon Dewitt, Grenville

Barnes, Ken Portier, and Loukas Arvanitis--have been very supportive. Their comments

and edits contributed substantially to my research and to this document. I particularly

want to thank Ken Portier, who always has time and enthusiasm for the students who

come to his office, and to Scot Smith, for his constant encouragement.

Franklin Percival, my immediate supervisor and Unit Leader of the Florida

Cooperative Fish and Wildlife Research Unit, and Nat Fraiser, Chairman of the

Department of Wildlife Ecology and Conservation showed tremendous understanding in

allowing me to pursue this degree while continuing at work. Their encouragement is also

greatly appreciated.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ............................ ................. ..............................................iii

LIST OF TABLES ............. ....... ...... ........ ....... ......................... vii

LIST OF FIGURES .................................................. ........... ............ xi

AB STR A CT ........... .. ...................................................................................... .............. xv

CHAPTERS

1 IN TR O D U C T IO N ....................... ........................... ............ .............

Im pacts of B razilian Pepper................................................. ............................... 1
Texture Analysis ...................................................................... ........ 3
G general A approach .................................................................................... 8

2 IM A G E C O L L E C T IO N ....................................................................... ...................10

Intro du action ..................................................... 10
M eth o d s ..................................................... 10
Camera Setup ....................................... .............................10
Field Collection of Aerial Imagery and Ground Surveys ........................................ 11
R e su lts ....................................................................... 1 3
Discussion ........................ .............. ..... 18

3 SPECTRAL NEIGHBORHOOD TEXTURES .................................. ...............19

M eth o d s ................... ................................. 2 1
F irst O rd er Statistics .............. .... .. .. .. .. ........................................ ................ .. 2 2
Normalized Gray Level Dependence Matrices (NGLDM).................................... 24
U nivariate Classification Trials ........................................ .......................... 27
R esu lts .............. .. ... .. ... ......... .. .. .................................................... 3 0
Signature D evelopm ent...................... .. .... ......... ......................... .. .................. 30
Univariate Calibration Image Classifications .................................. .............. 31
Univariate Validation Image Classifications .................................................... 33
D iscu ssio n ............. ......... .. .. ......... .. .. ................................................... 3 3




iv









4 E D G E T E X T U R E S ......... ...... ........... ................. ........................... .........................64

Introduction....................... ............... ..... ............. 64
M ethods.............................. ............. ...... 65
R e su lts .............. ..... ............ ................. ....................................................... 6 7
Image Signatures.............................. ............... 67
Univariate Calibration Image Classifications ....................... .............. 70
Univariate Validation Image Classifications ...................................................... 72
D iscu ssion ................. ...................................................................................... 73

5 MULTIVARIATE DISCRIMINATION AND CLASSIFICATION ..........................117

Intro du action ..................................................... 1 17
M eth ods ....................................................................................... 117
Multi-texutral Classification of Schinus ................ ..................... 117
Multi-textural Classification for all Vegetation Classes....................................... 122
R results ........................................................................ ........ .. .......... 123
Multi-texutral Classification of Schinus .................................. 123
Multi-textural Classification for all Vegetation Classes ............... ....... ... 127
D discussion ........................................ ........129

6 C O N C L U SIO N S ................................................................ 145

Sum m ary ...................................... .................. .......... .. 145
O their M odeling A approaches ....................................................................... 148
Other Texture A approaches ..................................... ............... ............... 150
M anagem ent Considerations ........................................... ............... .............. 151
F u tu re S tu d ie s ...................................................................................... 1 5 2

APPENDIX C++ PROGRAMS DEVELOPED FOR THIS DISSERTATION ............154

Sum m ary of Program s ............ .. ..... ... .. ... .... .... .. ................... ............ ...... 154
N ote to U sers............................. ............. ...... 155
T ex tu reM ak er ......... .... ......... ............... ...................................... 162
E d g eT ex tu res .................................................................................................... 17 6
E rd2A sc............................. .............. ..... 19 1
E rd2A scStratified ................. ............................... .............................................. 195
B atchE x tractC lass ........................................................................................ 2 02
E rd 2 S p lu s ....................................................................................................... 2 0 6
E rd 2 S A S ...................................................................... ..... 2 1 4
S A S R eco d e ........................................................................................... 2 19
SASMultiRecode ......................................... ................. 223
D ensityT threshold ......... ...................................................... .. ..... .. .......... .. 23 1
Uclassify ..................................... ............. .............. 234
A c c A ssE rd a s ............................................................................................ 2 4 2
ErrorM atrix .................. ................................... ... .... .. .... ........ ...... 246
LogRegressClassify ....................................................... .. 250


v









L ogRE rrorM atrix .................. ....................................... ............. .. 256
M ultiProbClassify ..................................... ................................. .. 261
ErdHistogram ............................. .............. 266
E rdasB andV iew er .. ........... .................................................... ................ .. .. 270
ErdasEnhance.................................... .............. 275

L IST O F R E FE R E N C E S ........................................................................ ...................283

BIOGRAPHICAL SKETCH .............. ......................... .................... ...............288
















LIST OF TABLES


Table Page

2-1. Frequency of occurrence of classes in the calibration images.................. ...............13

2-2. Frequency of occurrence of classes in the validation images...............................13

3-1. Decision vector for the signatures shown in Figure 3-3. .........................................28

3-2. Contingency table for wwl085 Mean textures classification .....................................51

3-3. Contingency table for ww1093 Mean textures classification .....................................51

3-4. Contingency table for ww1099 Mean textures classification. ........................................52

3-5. Contingency table for ww2042 Mean textures classification .....................................52

3-6. Contingency table for ww2046 Mean textures classification .....................................53

3-7. Contingency table for wwl085 Standard Deviation textures classification. .................55

3-8. Contingency table for ww1093 Standard Deviation textures classification. .................55

3-9. Contingency table for ww1099 Standard Deviation textures classification. .................56

3-10. Contingency table for ww2042 Standard Deviation textures classification. ................56

3-11. Contingency table for ww2046 Standard Deviation textures classification. ................57

3-12. Contingency table for 0212-528 Mean textures classification.....................................59

3-13. Contingency table for ww2008 Mean textures classification .....................................59

3-14. Contingency table for ww2060 Mean textures classification .....................................60

3-15. Contingency table for ww2091 Mean textures classification. .................... ...........60

3-16. Contingency table for 0212-528 Standard Deviation textures classification ...............62

3-17. Contingency table for ww2008 Standard Deviation textures classification. ................62









3-18. Contingency table for ww2060 Standard Deviation textures classification. ................63

3-19. Contingency table for ww2091 Standard Deviation textures classification. ................63

4-1. Contigency table for ww1085 Laplacian Edge Density texture classification ...............86

4-2. Contigency table for ww1093 Laplacian Edge Density texture classification ...............86

4-3. Contigency table for ww1099 Laplacian Edge Density texture classification ...............87

4-4. Contigency table for ww2042 Laplacian Edge Density texture classification ...............87

4-5. Contigency table for ww2046 Laplacian Edge Density texture classification ...............88

4-6. Contingency table for ww1085 Sorbel Density texture classification...........................90

4-7. Contingency table for ww1093 Sorbel Density texture classification............................90

4-8. Contingency table for ww1099 Sorbel Density texture classification............................91

4-9. Contingency table for ww2042 Sorbel Density texture classification ...........................91

4-10. Contingency table for ww2046 Sorbel Density texture classification .........................92

4-11. Contingency table for ww1085 Sorbel Edge Magnitude texture classification. ............94

4-12. Contingency table for ww1093 Sorbel Edge Magnitude texture classification. ............94

4-13. Contingency table for ww1099 Sorbel Edge Magnitude texture classification. ............95

4-14. Contingency table for ww2042 Sorbel Edge Magnitude texture classification. ............95

4-15. Contingency table for ww2046 Sorbel Edge Magnitude texture classification. ............96

4-16. Contingency table for ww1085 Sorbel Edge Slope texture classification...................98

4-17. Contingency table for ww1093 Sorbel Edge Slope texture classification...................98

4-18. Contingency table for ww1099 Sorbel Edge Slope texture classification...................99

4-19. Contingency table for ww2042 Sorbel Edge Slope texture classification...................99

4-20. Contingency table for ww2046 Sorbel Edge Slope texture classification.....................100

4-21. Contingency table for ww1085 Sorbel Edge Standard Deviation texture
classification. ......................................................................102

4-22. Contingency table for ww1093 Sorbel Edge Standard Deviation texture
classification .................................................................... 102









4-23. Contingency table for wwl099 Sorbel Edge Standard Deviation texture
classification .................................................................... 103

4-24. Contingency table for ww2042 Sorbel Edge Standard Deviation texture
classification. ......................................................................103

4-25. Contingency table for ww2046 Sorbel Edge Standard Deviation texture
classification. ......................................................................104

4-26. Contingency table for 0212-528 Sorbel Edge Density texture classification...............106

4-27. Contingency table for ww2008 Sorbel Edge Density texture classification ................106

4-28. Contingency table for ww2060 Sorbel Edge Density texture classification ................107

4-29. Contingency table for ww2091 Sorbel Edge Density texture classification ................107

4-30. Contingency table for 0212-528 Sorbel Edge Magnitude texture classification. ...........109

4-31. Contingency table for ww2008 Sorbel Edge Magnitude texture classification. ............109

4-32. Contingency table for ww2060 Sorbel Edge Magnitude texture classification. ..........110

4-33. Contingency table for ww2091 Sorbel Edge Magnitude texture classification. ..........110

4-34. Contingency table for 0212-528 Sorbel Edge Slope texture classification .................12

4-35. Contingency table for ww2008 Sorbel Edge Slope texture classification.....................112

4-36. Contingency table for ww2060 Sorbel Edge Slope texture classification.....................113

4-37. Contingency table for ww2091 Sorbel Edge Slope texture classification.....................113

4-38. Contingency table for 0212-528 Sorbel Edge Standard Deviation texture
classification. ................................................................... ..........115

4-39. Contingency table for ww2008 Sorbel Edge Standard Deviation texture
classification. ................................................................... ..........115

4-40. Contingency table for ww2060 Sorbel Edge Standard Deviation texture
classification. ................................................................... ..........116

4-41. Contingency table for ww2091 Sorbel Edge Standard Deviation texture
classification. ................................................................ ... ..........116

5-1. Logistic regression coefficients for Schinus terebinthifolius derived from the
calibration im ages. ........................ ....... ...... ...... ...............124

5-2. Accuracy assessment for images with a classification threshold at 50% probability......124









5-3. Accuracy assessment for images with a classification threshold at 30% probability......125

5-4. Commission errors for Schinus terebinthifolius in images that do not contain
Schinus...................................................... ................... ... .... ... .... . 126

5-5. Logistic regression variables and coefficients for the additional vegetation cover
ty p e s. .......................................................................... . 13 6

5-6. Contingency table for ww1085 logistic regression classification.............................139

5-7. Contingency table for ww1093 logistic regression classification.............................139

5-8. Contingency table for ww1099 logistic regression classification.............................140

5-9. Contingency table for ww2042 logistic regression classification ................................140

5-10. Contingency table for ww2046 logistic regression classification .............................141

5-11. Contingency table for 0212-528 logistic regression classification..............................141

5-12. Contingency table for ww2008 logistic regression classification .............................142

5-13. Contingency table for ww2060 logistic regression classification .............................142

5-14. Contingency table for ww2091 logistic regression classification ............................143
















LIST OF FIGURES


Figure Page

1-1. Comparison of spectral response in three similar wavelength bands for imagery from
Landsat Thematic Mapper satellite and a high resolution aerial digital camera...4

2-1. Images used in testing and calibration of textures and their corresponding
classification masks.................... .............. ..............14

2-2. Images used for validation of texture classifications and their corresponding
reference classifications. ........................................ ....................................... 16

3-2. Creating the Normalized Gray Level Dependence Matrix............................................25

3-3. Combined signatures for the M ean Texture. .......................................... ............... 28

3-4. Illustration of accuracy assessment measures ................................................29

3-5. Spectral signatures from the infrared image band................ .............. ............... 36

3-6. Spectral signatures from the red image band ....................................... ............... 37

3-7. Spectral signatures from the green image band.............. .............. ................38

3-8. ww2042 spectral signatures of the first-order textures with a window size = 7x7. ........39

3-9. ww2042 spectral signatures of the first-order textures with a window size = 15x15.....40

3-10. ww2042 Spectral signatures of the first-order textures with a window size = 3 1x31...41

3-11. ww2042 Spectral signatures of the first-order textures with a window size = 63x63...42

3-12. M ean texture signatures ........................................................................ ...................43

3-13. Standard Deviation texture signatures. ............................................... ............... 44

3-14. Skew texture signatures ........................................................................ ...................45

3-15. Entropy texture signatures. ................................................ ................................ 46









3-16. H om ogeneity texture signatures .................................. ............... ............... 47

3-17. Simpson's Diversity texture signatures. ..................................... .....................48

3-18. Normalized Gray Level Difference Matrix contour plots for vegetation covers in
im age w w 2042. ....................... ...................... ........................ 49

3-19. Classification of calibration images from the Mean texture. .......................................50

3-20. Classification of calibration images from the Standard Deviation texture...................54

3-21. Classification of validation images from the Mean texture.........................................58

3-22. Classification of validation images from the Standard Deviation texture....................61

4-1. V alues used in 3x3 edge filters................................................. .................................65

4-2. Exam ple Sorbel edge im ages ................................................ .............................. 76

4-3. Exam ple Laplacian edge im ages. ............................................ ............................ 77

4-4. Sorbel and Laplacian image histograms from edge images ofwwl085........................78

4-5. Laplacian Edge Density texture signatures. ........................................ ............... 79

4-6. Sorbel Edge Density texture signatures ........................................ ........ ............... 80

4-7. Sorbel Edge Magnitude texture signatures....................... ... .......................... 81

4-8. Sorbel Edge Slope texture signatures. ........................................ ......................... 82

4-9. Sorbel Edge Standard Deviation texture signatures......................................................83

4-10. Image Standard Deviation texture signatures ..............................................84

4-11. Classification of calibration images from the Laplacian Edge Density texture .............85

4-12. Classification of calibration images from the Sorbel Edge Density texture...................89

4-13. Classification of calibration images from the Sorbel Edge Magnitude texture..............93

4-14. Classification of calibration images from the Sorbel Edge Slope texture ...................97

4-15. Classification of calibration images from the Sorbel Edge Standard Deviation
texture ............................................................... .... ..... ......... 101

4-16. Classification of validation images from the Sorbel Edge Density texture..................105

4-17. Classification of validation images from the Sorbel Edge Magnitude texture .............108









4-18. Classification of validation images from the Sorbel Edge Slope texture.....................111

4-19. Classification of validation images from the Sorbel Edge Standard Deviation
texture ............................................................... ..... ..... ......... 114

5-1. Logistic regression classifications for Schinus terebinthifolius in the calibration
im ag e s...................................................................... . 1 3 2

5-2. Logistic regression classifications for Schinus terebinthifolius in the validation
im ag e s...................................................................... . 1 3 4

5-3. Classification of calibration images from logistic regression of each vegetation
ty p e .............................................................................. 1 3 7

5-4. Classification of validation images from logistic regression of each vegetation type. ....138

5-5. Logistic regression probabilities for hardwood in image ww1085..............................143

5-6. Examples of logistic regression classification of each vegetation type when
hardw oods w ere excluded .............................................................................. . ...144

A-1. Screen shot of TextureM aker program .............. .................................... ............... 162

A-2. Screen shot of EdgeTextures program ...................................................... ...............176

A-3. Screen shot of Erd2Asc program ........................................... ............................ 191

A-4. Screen shot of Erd2AscStratified program. ....................................... ............... 195

A-6. Screen shot of BatchExtractClass program .......................................... ............... 202

A-7. Screen shot of Erd2Splus program ...........................................................................206

A-8. Screen shot of Erd2SA S program ............................................................................ 214

A-9. Screen shot of SA SRecode program ........................................ .......................... 219

A-10. Screen shot of SASMultiRecode program. ...................................... ............... 223

A-11. Screen shot of DensityThreshold program. ........................... .................. ...............231

A-12. Screen shot of Uclassify program ...........................................................................234

A-13. Screen shot of AccAssErdas program ............ ..................................... .................242

A-14. Screen shot of ErrorM atrix program ............................................................. .......... 246

A-15. Screen shot of LogRegressClassify program. .................................... .................250









A-16. Screen shot of LogRErrorMatrix program. ....................................... ............... 256

A-17. Screen shot of MultiProbClassify program................. ...........................................261

A-18. Screen shot of ErdHistogram program ................................... ........................ ......... 266

A-19. Screen shot of ErdasBandViewer program....................................... ............... 270

A-20. Screen shot of ErdasEnhance program. ........................... ..................... 275















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

DISCRIMINATION OF AN INVASIVE PLANT, Schinus terebinthifolius, FROM
AERIAL DIGITAL IMAGERY

By

Leonard G. Pearlstine

December 2000

Chairman: Dr. Scot E. Smith
Major Department: Department of Civil and Coastal Engineering

Schinus terebinthifolius, known as Brazilian pepper, is an exotic, invasive plant

species in Florida that displaces native plant species to the detriment of beneficial native

plant and animal habitat in aquatic, wetland, and terrestrial environments. Rapidly and

accurately identifying invasive species is critical to successful monitoring and

management of ecosystem change. Aerial surveys typically are used for this purpose, but

observers or photointerpretation may be inconsistent and costly. Image processing for the

classification of land cover types that relies on by-pixel spectral response of the different

vegetation in an image to discriminate the cover types is problematic. In high-resolution

imagery, adjacent pixels of the same cover type are likely to represent different parts of

the plant with very different spectral responses. Measuring spatial patterns of texture in

neighborhoods of pixels can be an effective alternative.

Textures derived from first-order statistics, co-occurrence matrices, and

components of edge in high-resolution digital color infrared images were tested for their









application to the discrimination of Schinus terebinthifolius. Logistic regression on the

texture variables found the combination of the mean, Sobel edge density, Sobel edge

magnitude, and edge standard deviation to be the best discriminators of Schinus in these

images with a median user's accuracy of 87.3 percent and producer's accuracy of 44.6

percent. The high user's accuracy and lower producer's accuracy reflect the model's

consistent identification of core areas of Schinus, but not the edges of Schinus patches.

Misidentification of other cover types as Schinus was extremely low except where Sabal

palmetto was present in the images. Contextual discrimination between Schinus and

Sabal requires more exploration. Logistic regressions also were used for combined

classifications of all cover types in an image. User accuracies for Schinus generally

remained high, but omission errors increased in cases where Schinus was misidentified as

one of the other cover types.














CHAPTER 1
INTRODUCTION


The objective of this dissertation was to develop multivariate image textural

feature classification approaches for rapid, unsupervised identification of Brazilian

Pepper, Schinus terebinthifolius, from aerial low altitude, color infrared imagery. Rapidly

and accurately identifying land covers is critical to successful monitoring of ecosystem

change. The identification of invasive and nonnative species is particularly important.

Invasive and nonnative species may severely disrupt native ecosystem functions and

displace wildlife habitats. Thirty percent of Florida's flora, or approximately 1000

species, is exotic. Sixty five of those species are listed as Category I by the Florida Exotic

Pest Plant Council (1999). These Category I species are known to disrupt native plant

communities in Florida. They displace native plant species to the detriment of beneficial

native plant and animal habitat in aquatic, wetland, and terrestrial environments. Another

59 plants are Category II species: potentially disruptive, but ecological damage is not yet

demonstrated. Successful monitoring, management, and eradication of invasive plants is

critical to maintaining native habitats in Florida.



Impacts of Brazilian Pepper

Brazilian pepper, a Category I plant species, was introduced at the turn of the

century (Morton 1978) or possibly earlier (Ferriter 1997) as an ornamental shrub. It

earned the name 'Florida holly' because of its bright red fruits and shiny leaves. In its









native South and Central American range, Brazilian pepper occurs as scattered

individuals in habitats from sea level to over 700 m elevation (Ewel et al. 1982). In south

and central Florida, Brazilian pepper can dominate the landscape as monotypic stands in

a broad range of moist to mesic habitats (Ewel 1986). The surveys of the South Florida

Water Management District indicate that Brazilian pepper occupies more than 280,000 ha

(700,000 ac) in Florida and it is the most widespread exotic in the state (Ferriter 1997).

Characteristics that make Brazilian pepper a successful opportunistic species

include fast growth, prolific seed production, near continuous shoot extension and leaf

renewal, vigorous resprouting, tolerance of a wide range of growing conditions, seed

distribution by forging birds, and shade tolerant seedlings (Ewel 1979). The latter

characteristic is particularly unusual in weedy species. These characteristics result in an

aggressive plant species that has invaded many of Florida's native plant communities and

disturbed sites such as highway and canal right-of-ways, fallow fields, and drained

cypress stands (Woodall 1982). Wildlife and plant diversity is threatened when Brazilian

pepper colonizes large areas of the state, displaces native species, and eliminates many

indigenous sources of food for wildlife (Morton 1978). For example, Curnutt (1989) has

documented declines in avian diversity and population density. Direct contact with the

sap can cause severe and persistent skin irritation. Brazilian pepper is a relative of poison

ivy. It is a hazard for rangelands where consumption of foliage by horses and cattle can

cause hemorrhages, intestinal compaction, and fatal colic (Morton 1978).

An approach to surveying and monitoring exotic species such as Schinus

terebinthifolius that 1) uses unmodified small fixed wing aircraft, 2) is ready for

evaluation immediately after the flights, 3) provides archival documentation of the exotic









species' existence and location, and 4) provides automated computer discrimination of

the target species with statistically determined confidence levels would benefit natural

resource managers and researchers attempting to document and control exotic plant

expansion. This study focused on the last item: automated computer discrimination of a

target species, Schinus terebinthifolius, from low altitude aerial digital imagery.



Texture Analysis

Image spectral tone (grey level) differences among vegetation and land covers are

often used to create signatures of the cover types in imagery of the earth's surface. These

spectral signatures may be compared to identify the likely cover type of each pixel in the

image. This pixel-by-pixel classification approach becomes more problematic as image

resolution increases because more individual features are discriminated and the variance

in spectral values observed within a cover type increases. For example, at spatial

resolution below 1 meter, imagery of a forest or shrub type would return many pixels of

leaf surfaces from different angles and different degrees of shading, stems, and possibly

various ground covers under the trees or shrubs. Individual pixels representing one of

these features may vary considerably in spectral reflectance from a pixel representing one

of the other features of the same cover type. This makes it impossible to classify a pixel

to a cover type based on its spectral tone. Spectral tone from the various features of a

cover type is averaged as images decrease in spatial resolution. Lower resolution images,

such as Landsat and SPOT satellite imagery, are often successfully classified from pixel-

by-pixel identification of a spectral signature (Lillesand and Kiefer 2000, Jensen 1986)

because the reflectance within a cover type averaged over a multi-meter square pixel is

generally more homogeneous across the pixels in the imagery. Figure 1-1 illustrates this







4


problem with sample images of Schinus terebinthifolius. A Landsat image of Schinus


shows low variance and sharp peaks in the spectral response of Schinus, making it a good


candidate for spectral tone classification. A high-resolution image of Schinus, however,


has many pure pixels of light glint off the tops of leaves and other pixels of deep shadow


below the leaves. Its tonal response extends over the entire spectral range of the image.


Figure 1-1. Comparison of spectral response in three similar wavelength bands for
imagery from Landsat Thematic Mapper satellite and a high resolution aerial digital
camera. The Landsat image has a spatial resolution of 28.5 m vs 5 cm for the digital
camera image. The bands are blue line: blue wavelengths (TM 2), magenta line: red
wavelengths (TM 3), and yellow line: near infrared wavelengths (TM 4).


Photo-interpreters rely on many clues for classification in addition to spectral


tone. Texture, shape, and context of features in the image are elements that go beyond the


individual pixel to the neighborhood of features surrounding the pixel. When a pixel is


viewed in context with its neighboring pixels, a pattern may emerge that is consistent


with a cover type and different from other cover types. This sort of contextual


Landsat TM Schinus Signature

1.20 -


5 0 ..0 . ......... ........... ..- ........ .... ........ ........ ..... ........
01.0 .................... .. ..................................... .:'.I.Z.I.I-I.I. .I-I. .I. .Z.I. .: .*.:..-...-......-.:.

S . . ........................................
m 0 .20 -
. ............. ., .. ...O .. .. .


Refledance Value
ii
Li- SD
6 :oc n""""""""""
"' Al____ __ """""""""
a020" " " "" " "" "
om" "" " "" "
ci :,tc Co ct CO 81
Reflectance Viue ""


Digital Camera Schinus Signature

1.320
S1.00 -
g- 0.80 --

a I
--: 0.60 ,--'*.--..- ,-- .. .....-- -- -
0.03

e Om f 'C D -nCO C, o-M
0 i M CO c W t N C C
Reflectance Value









identification can improve the accuracy of satellite image classification (Kushwaha et al.

1994, Irons and Petersen 1981) and is essential for classification of high-resolution

images.

Haralick et al. (1973) defined texture as the statistical properties of the spatial

distribution of gray tones (varying shades of gray in the resolution cells) in an image.

Texture is composed of patterns of these gray tones that are smaller than the area where

they are repeated (Sun and Wee 1983). Texture analyses to improve classification of

satellite imagery and aerial photography have been applied to forested landscapes

(Holopainen and Wang 1998, Wulder et al. 1998, Riou and Seyler 1997, Kushwaha et al.

1994), wetlands (Yamagata and Yasuoka 1993), landuse (Emerson et al. 1999, Irons and

Petersen 1981), and geomorphology (Shih and Schowengerdt 1983). Texture analyses

have also been applied to sets of close-up "natural" images such as the Brodatz texture

images (Brodatz 1967) of sand, pebbles, cow hide, fieldstones, and other regular textures

(Potlapalli and Luo 1998, He and Wang 1991), and even images of patterns on animal

and plant surfaces (Kiltie et al. 1995).

No single texture or contextual approach has emerged in the last 3 decades of

computerized texture analysis as superior in its capability to improve classification

accuracy. Two of the most common approaches are first- and second-order statistical

textures. First-order statistics such as the mean, standard deviation, and skew quantify the

distribution properties of spectral tone in the image. Second-order statistics describe the

frequency with which one gray tone appears in specific relationship to another gray-tone

on the image (Haralick et al. 1973) through the construction of relative frequency

distribution matrices. Second-order statistical textures also are referred to as spatial co-









occurrence textures. Yamagata and Yasuoka (1993) obtained enhanced classification

performance in SAR microwave imagery of wetlands using the co-occurance matrix

textures. Microwave images characteristically contain speckle noises that confuse

conventional classification methods. Kushwasha et al. (1994) reported modest

improvements in IRS satellite classification of forests using Haralick textures, but Dikshit

(1996) obtained better results with first-order statistics (airborne Thematic Mapper

images). Both authors observed better accuracies with a combination of tone and texture

layers rather than the spectral tone layers alone. Irons and Petersen (1981) as well as Hsu

(1978) had earlier results suggesting that higher order statistics do not improve the

texture classification for Landsat MSS or aerial photography (high and low altitude),

respectively. Bijlsma (1993) examined first- and second-order texture measures with

respect to herbaceous vegetation structure (i.e., species richness and structural diversity)

rather than cover type. For this application, Bijlsma found textures derived from second-

order statistics had no meaning, and, in fact, the simplest model, containing the mean

texture only, yielded the best results.

Some other texture analysis approaches include experimentation with Fourier

transformations (Riou and Seyler 1997, Coggins and Jain 1985, Matsuyama et al. 1980)

and their more recent superset, wavelets (Kiltie et al. 1995, Unser 1995). He and Wang

(1991) observed that traditional Fourier transformation is better in characterizing absolute

intensity relationships and spatial relationships of pixels in a large scale, whereas texture

analysis is interested in relative intensity relationships among pixels in a small

neighborhood. They advocated a textural spectrum method for analysis.









Semivariograms measure spatial variability and spatial continuity in an image.

Spatial continuity (i.e., data that are close together are more likely to have similar values

than data that are far apart) exists in most earth science data sets (Isaaks and Srivastava

1989). Carr and De Miranda (1998) reported that textures based on the semivariogram

result in better classification accuracies for microwave imagery but are less effective than

the second-order Haralick textures for optical images. Wulder et al. (1998), however,

reported significant increases in accurate classification of aerial imagery when the

semivariogram was combined with second-order statistical textures vs. the use of either

texture alone.

Fractals are another relatively new approach to texture analysis that can provide a

measure of surface roughness. Potlapalli and Luo (1998) and Dennis and Dessipris

(1989) have demonstrated its applicability with Brodatz textures. Dennis and Dessipris

also presented a computationally efficient approach to generating the fractal information

layer. Emerson et al. (1999) demonstrated fractal textures computed from the NDVI of

high-resolution aerial imagery. When images were degraded from 10 m to 80 m spatial

resolutions, agricultural land cover increased in fractal dimension, forested land cover

grew smoother, and urban areas remained roughly self-similar.

As Ryherd and Woodcock (1996) observed, texture is only a useful addition when

classes in the images exhibit differences in spatial tone patterns. The authors also

observed that for the range of remote sensed images they tested, results never were

degraded when textural information layers where combined with the spectral bands.

The literature is replete with comparisons of single new texture approaches to

other, more established approaches such as the Haralick co-occurrence matrix textures.









When textures are used in combination with other bands of information, it is most

commonly with the original spectral bands. Rarely have multiple textures been tested for

the best combination to discriminate land covers. A premise of this study was that

multivariate discriminate function analysis yields results superior to those obtained using

only a single texture. Several features are believed to be desirable for textures selected in

this study. Textures that are invariant to rotation are necessary because vegetation does

not always grow in the same orientation and planes do not always fly in one direction.

Textures that are invariant to gray level transformations also are desirable because

lighting conditions and camera parameters can vary between images. Finally, simple

algorithms for computational efficiency and ease of transfer to other applications are

most suited to the objective of this study.



General Approach

Low altitude, digital aerial photography was collected over areas containing

Schinus terebinthifolius and other vegetation likely to occur in the vicinity of Schinus.

First-order, second-order, and edge-texture analyses were conducted with these images,

and texture signatures generated for each of the vegetation types were observed. Texture

values are computed for each pixel in an image by examining the neighborhood

surrounding that pixel, i.e., a moving window. Optimal window size is one of the issues

that must be resolved for a successful class identification. A small neighborhood is

computationally faster and may capture higher frequency changes. A larger neighborhood

may better represent the pattern of texture in a class. However, the larger neighborhood

typically smoothes the resulting image, thus reducing the ability of detecting accurate

class boundaries. Single- and multiple-texture classifications were conducted to test









classification accuracies of each texture and the best combination of textures to maximize

discrimination of Schinus from the other image cover types. Non-parametric methods

were used for classification. Single-texture classifications proceeded by comparison of

the density functions of texture signatures for the cover types and selection of the highest

density class for the texture value at each location. Multivariate class discrimination was

accomplished with a "best" combination of texture variables by best subset selection with

logistic regression and backward elimination stepwise logistic regression. Classification

success was evaluated with the now-standard method of reporting contingency tables and

their associated producer and user accuracies as presented in Story and Congalton (1986)

and Congalton and Green (1999).














CHAPTER 2
IMAGE COLLECTION



Introduction

The Florida Cooperative Fish and Wildlife Research Unit acquired high-

resolution digital aerial imagery to assist land cover mapping. Because individual plant

species often can be visually identified on the imagery, there appeared to be a potential to

use the imagery to establish the identification of invasive plant species. Images from

these flights were selected, and the cover types in them were mapped to test the potential

for computer recognition of the cover types.



Methods

Camera Setup

A Kodak DCS420 CIR 24-bit digital color infrared digital camera with a 1012 x

1524 fixed plane array was mounted on a Cessna 172 aircraft. An Accuphoto aerial

photography control system took input from an on-board Garmin 12-channel GPS and a

Watson Industries Attitude and Heading Reference System (AHRS-BA303). This device

utilizes three solid state attitude gyros to provide two attitude angles and a third heading

angle. An accuracy of 0.3 degrees in attitude angles determination can be achieved using

this instrument (Watson Industries, 1995). The GPS received real-time differential

correction signals from a DCI FM receiver. The control system used input from the GPS

to trigger the digital camera and at the same time record the GPS and attitude









information. This system was used to calculate the geo-location of each frame of the

aerial imagery. Geolocations were not required for the image processing objective of this

study. However, geolocations would be critical for any monitoring applications using the

results of this research.


Field Collection of Aerial Imagery and Ground Surveys

Aerial images from this setup were collected for testing and calibration of texture

signatures. The images were selected to provide a variety of vegetation covers that are

likely to occur adjacent to Schinus terebinthifolius. They were also selected to provide

images of Schinus as large clusters as well as individual plants. Five images were

selected from a series of flights conducted in October 1997 over Cape Canaveral National

Seashore, on the east coast of Florida (- 80 37'W, 280 40'N). The vegetation

composition of the images is as follows:


1. ww2042 Brazilian pepper, cattail, wax mrytle, and open water

2. ww2046 hardwoods, sabal palm

3. ww1093 Brazilian pepper, black mangrove, herbaceous, open water

4. ww1099 Brazilian pepper, shrub, herbaceous

5. ww1085 sabal palm, herbaceous


where Brazilian pepper is Schinus terebinthifolius, cattail is Typha spp., wax mrytle is

Myrica cerifera, black mangrove is Avicennia germinans and sabal palm is Sabal

palmetto.

All of the images were flown at approximately 366 m above terrain with a 135

mm lens yielding a 37 x 25 m ground footprint and a spatial resolution of 2.4 cm. All









images were cropped to an area of interest to reduce processing times and storage

requirements.

Visits to Cape Canaveral included the sites of the ww2042 and ww1093 images.

These site were visited to confirm the vegetation types interpreted to occur on the images.

Accuracy assessments were conducted using three additional images from the

same date over Cape Canaveral and one image from February 2000 containing Schinus

over an area south of Tampa, near Ruskin on the west coast of Florida (- 82o 24'W, 27o

40'N), 60 miles south of the Cape Canaveral site. The southern flightlines were

overexposed and lost much of the image detail. One image (image 0212-528) was

retained to test the limits of the sensitivity of texture analyses. These images are referred

to as "validation images" in the remaining chapters. The vegetation composition of the

validation images is as follows:

1. 0212-528 Brazilian pepper and Brazilian pepper stressed by herbicide

2. ww2008 Brazilian pepper and herbaceous ground cover

3. ww2060 Brazilian pepper, marsh, and water

4. ww2091 sabal palm, hardwoods, and water

To compare the texture results to specific vegetation covers, a raster mask file was

created that classifies the original images into their known vegetation types for both the

calibration images and the validation images. The masks were created by supervised

classification in Imagine image processing software (ERDAS, Inc.) followed by

extensive on-screen digitizing to correct and clean up the final classification. These

classifications are referred to in the remaining chapters as "reference classifications" or

"reference masks."











Results

The images selected for testing and calibration of textures are shown in Figure 2-

1. To the right of each image is the reference classification. Table 2-1 presents the

frequency of occurrence of each of the cover types in the calibration images. The

validation images and their corresponding reference classifications are shown in Figure

2-2. Table 2-2 includes the frequency of occurrence of each cover type in the validation

images.


Table 2-1. Frequency of occurrence of classes in the calibration images.


CLASS


ww1085 ww1093 ww1099 ww2042 ww2046


Table 2-2. Frequency of occurrence of classes in the validation images.

ICLASS [2012-528 ww2008 ww2060 ww2091


Unclassified
Schinus terebinthifolius
Sabal palmetto
Open Water
Avicennia germinans
Herbaceous
Shrub

Typha spp./ Marsh
Myrica cerifera
Hardwoods


22.9% 2.4%
77.1% 48.2%


15.1%

31.9%
25.9%


49.4%


18.9%


27.1%


Unclassified 7.2% 27.1% 3.9% 5.2% 49.4%
Schinus terebinthifolius 32.0% 30.0% 34.1%
Sabal palmetto 3.7% 0.9% 48.3%
Open Water 15.0% 20.5%
Avicennia germinans 8.7%
Herbaceous 17.2% 51.4% 2.3%
Shrub 14.7%
Typha spp. 23.3%
Myrica cerifera 16.0%
Hardwoods 89.1%


29.7%
8.8%

42.6%


























WWZU4Z


WW1UY.i


Figure 2-1. Images used in testing and calibration of textures and their corresponding
classification masks. The reference classifications show a 31 pixel buffer around each
class. Masks with 63 pixel buffers were also created for each image.










;rfLWW9Q
a -~

r


Legend
Schinus terebinthifolius
Myrica cerfera
Typha spp.
Sabal palmetto
Water


D Herbaceous
SScrub
D Avicennia germinans
Mixed Hardwoods
SUnclassified


Figure 2-1 continued.


wwl Q099


wwZU46


U
U
m
U
U


r.,:C ~XI
d.
9;










































WWZUUS


ww2060


Figure 2-2. Images used for validation of texture classifications and their corresponding
reference classifications.


















Legend
0 Schinus terebinthifolius Herbaceous
D Stressed Schinus t. Scrub
Typha spp. Mixed Hardwoods
Sabal palmetto Unclassified
SWater


Figure 2-2 continued.














Discussion

The calibration and validation images depict a diverse set of vegetation cover

types from forested to herbaceous that is likely to occur in images with Schinus

terebinthifolius. The images also portray Schinus in densities ranging from individual

bushes to large clumps. These images provide a good range of conditions for testing

texture analysis for classification.














CHAPTER 3
SPECTRAL NEIGHBORHOOD TEXTURES


Introduction

First-order statistical textures and second-order or co-occurrence statistical

textures are explored in this chapter with regard to their potential to discriminate Schinus

terebinthifolius and other cover types. First-order statistics are simple continuous

measures of variability such as the mean, standard deviation, and skew.

Second-order statistical textures are used to measure variability of values in a

neighborhood in relationship to the other values in a neighborhood. This approach

requires that a co-occurrence matrix be generated from which the textures are derived.

The most cited example of second-order, co-occurrence textures are the Haralick

textures. Haralick et al. (1973) computed "angular nearest-neighbor gray-tone spatial-

dependence matrices" as the relative frequency, P,,, with which two neighboring cells,

one with gray tone, i, and the other with gray tone, j, occurred on the image. Separate

matrices were computed for the horizontal, vertical and two diagonal directions. Texture

features extracted from the matrices measured image homogeneity, contrast, coarseness,

and other spatial properties.

The Haralick textures require that images being compared are normalized to one

another so that gray tones of like features in the different images produce the same tones.

Haralick et al. (1973) suggested an involved equal-probability quantizing procedure

before texture analysis. Another drawback of many of the Haralick textures measures is









their angular dependence. Because textures must be angularly independent before they

can be used for classification, Haralick et al. suggested using the average and range of the

textures taken in the four directions. In 1983, Sun and Wee presented an alternative

neighboring gray level dependence matrix (NGLDM) for generating the co-occurrence

matrices and texture features that considers the relationship between a cell and all of its

neighboring cells at one time instead of one direction at a time. In addition to being

angularly independent, Sun and Wee stated that their approach is invariant under linear

gray level transformations, eliminating the need for image normalizations. The Sun and

Wee procedures are more computationally efficient because the neighborhood

relationships are computed once rather than 4 times and because the generated individual

matrix requires fewer operations than that of a single directional Haralick matrix.

Texture distributions within a particular cover type are referred to as "signatures"

for that cover type throughout this dissertation. The texture distributions or signatures

generated in this chapter and in Chapter 4 are transformed to kernel density estimates for

comparison and classification applications. The kernel estimator approximates the

distribution of the data by the summation of smooth, symmetrical functions placed at

each observation (Silverman 1986). Kernel density estimation is used rather than the

original data distribution because it produces a distribution with an area equal to 1 (i.e.,

integral of K(x)dx = 1) and is, therefore, a probability function necessary for modeling.

The kernel function also smoothes through the usual spikiness of the original data

distribution making a model more suitable for generalization.









Methods

First-order and NGLDM textures were computed for each of the three bands in

each of the five calibration images. The texture values are computed in a rectangular

moving window or neighborhood. The center cell in a neighborhood is assigned a value

that is the computed texture for that neighborhood. The window then is moved over one

pixel, and a new value is computed for that neighborhood. The window continues to

move across the image assigning texture values to the center cell of the window until the

entire image has been scanned. The texture programs described below assign values to all

the cells in the image by "mirroring" the edges. Because analysis windows centered on

cells near the edge of an image extend beyond the image, values are assigned to the

empty cells in the window by assuming that values just outside the image are the same as

values just inside the image and replicating those values to the empty cells (Figure 3-1).

The size of the moving window was varied from 7 pixels to 63 pixels in the ww2042

image to find the smallest analysis window in which texture discrimination remained

effective (if the texture is ever effective). The selected window size was applied to

analysis of the other images.


Min 25 7 |Figure 3-1. Mirroring image values
Moving Window .
Io in a moving window. This example
S -shows a 5 x 5 pixel moving window
-centered at the upper left corer of
I I the image. Textures computed from
I the 25 values in this window will be
5 3 3 2 recorded to the center pixel (grayed
cell).









Unless otherwise noted, the programs used in this chapter were written by the

author in C++ using the Borland Builder programming environment. The source code is

listed in the Appendix.

Output from each of the procedures below is a multiple band image file (Erdas 7.x

lan format) with each band being one of the texture images. The texture results were

compared to specific vegetation covers using the reference masks that classify the

original images into their known vegetation types (Chapter 2). The program Erd2Splus

produces an ASCII text file of randomly sampled texture values in a format for import to

the S-Plus statistical package (MathSoft, Inc.) for density estimation and plotting.

Erd2Splus uses the mask file to stratify the output by vegetation type. The ERDAS

Imagine Search routine and ModelMaker tools (ERDAS, Inc.) were used to create

versions of the mask that differentiate between vegetation near edges to other vegetation

types and vegetation away from edges (32 or 64 pixels inside the edge between classes).


First Order Statistics

The author-created program TextureMaker was used to generate first order

statistical textures for the mean, standard deviation, skew, entropy, and Simpson's

diversity. The algorithms are presented here.

From Tomaita and Tsuji (1990):

n
Mean = ip,
1=1


Standard Deviation = I(i p)2p,


n
Third Moment (Skewness) = (i P)3p,
1=1









n
Entropy= p, log p,
1=1

where i = the observed spectral intensity

n = the number of observations

p, = the probability of i occurring,

i.e., p, = number of points at intensity i / total number of points

S= mean of the intensities


The third moment measures the skew of the histogram of the intensities. When the

third moment is zero, the histogram is symmetric. The value is negative when the

histogram is skewed to the left and positive when the histogram skews to the right

(Tomaita and Tsuji, 1990). Entropy is a measure of the distribution of the intensities. Its

value approaches zero as the distribution becomes more clumped and its values increase

as the distribution becomes more uniform.

The Entropy metric is also known as Shannon's Diversity Index (McGarigal and

Marks, 1995). Shannon's Index uses the natural log and returns a value based on

information theory that represents the amount of "information" in the image (Shannon

and Weaver, 1949). The absolute magnitude of value is less useful than the relative

magnitude used for comparison (McGarigal and Marks, 1995).

Another diversity index is Simpson's Diversity Index:

n
5. Simpson's = 1.0- p,2
1=1

Simpson's Diversity Index represents the probability that intensities selected at

random would be different. The probabilities can be interpreted in both absolute and









relative terms. Simpson's Diversity Index is more intuitive than Shannon's and less

sensitive to rare values (McGarigal and Marks, 1995).


Normalized Gray Level Dependence Matrices (NGLDM)

There are two steps to creating an NGLDM for computing textures (Sun and Wee

1983). The first step is to find the NGLDM numbers, s, themselves. Within a moving

window neighborhood, s is equal to the number of values that are the same as the center

value. A large s suggests a smooth texture and a small s suggests a rough texture around

the center value. The output of this step is referred to as the table P. Next, the NGLDM,

Q, is constructed where Q(k,s) is the total number of entries in P that have gray level k

and NGLDM number s. The Q matrix is the frequency histogram of grayness variation in

the image. If a texture is coarse, it is likely that a pixel and its neighboring pixel have

similar gray level values so the occurring frequencies of large NGLDM numbers will be

higher than the occurring frequency of small NGLDM numbers. For a fine texture, the

NGLDM number is likely to be small because a pixel is likely to have a different gray

level value from its neighboring pixels. Therefore, the more large numbers are

concentrated on the right-hand side of the Q matrix, the coarser the image. The higher

the frequency counts on the left-hand side of the Q matrix, the finer the image texture.

Figure 3-2 illustrates the creation of an NGLDM.

In the TextureMaker program, the P table is generated for the entire image by

recording the NGLDM number, s, in a small window about each pixel. The Q matrices

are then generated by creating a frequency count of NGLDM numbers in a new, larger













4 3 6 5 4 1 (4 P,1) R .2) ,1) 4.t (1)


3 5 6 3 3 3 (j) (55. (6.3 P, p, P.2)
5 5 6 1 3 1 (2) 5,2) (6,) (1.1) .3) (1,2
3 3 8 1 1 t1 P,1) E. .- 3 3) (185] 81,3

2 0 8 1 8 1 1o0I.f) Qi, M .Q.1) .12)




a. b. C.


NOLDO NuImbs, s
0123 6B7 B
0100000000
0 1 0 0 0 42 0 D D

210 000 D 0 D 0 O
3043200000


502300000
6011300000
70000 D 000 0 D
.0 1. 2 1 0 0 0 00


Figure 3-2. Creating the Normalized Gray Level Dependence Matrix. (a.) Gray values in
original image. (b.) P table generated from a. showing (k,s) values (note that no edge
mirroring was used) in a 3x3 pixel window about each pixel. (c.) Q matrix from b.
showing frequency count of P(k,s) values. Adapted from Sun and Wee (1983).



moving window centered on each pixel in the image. Additionally, whether neighboring

pixels appear different or not can be affected by inning of the original data. The original

image has a possible 256 gray levels. If, for example, the image data are binned into 64

possible values, then gray levels that were originally different but close together would

often be assigned the same gray level. Because the window size used for the P table, the

window size of the Q matrix, and the inning range can all influence the resulting

textures, sensitivity tests were run on the ww2042 image to find suitable values for

application to the other images.

Sun and Wee (1983) discuss five textures generated from the NGLDM Q matrix

which are analogous to the Herelick et al. (1973) textures. These textures are replicated in

the program TextureMaker.









K S K S
Small Number Emphasis: N1 = I [Q(k,s)/s2] 1 Q(k,s)
k=l s=l k s=l



K S K S
Large Number Emphasis: N2 = _[s2Q(ks)] Q(k,s)
k=l s=l k=l s=l



S K 2 K S
Number Non-Uniformity: N3 = I YQ[k, s) Q(k, s)
s=l k=l k s=l



K S K S
NGLDM Second Moment: N4= [Q(k, s)]2 Q(k,s)
k=l s=l k=l s=l



K S IK S
NGLDM Entropy: N5= I ,Q(k,s)log[Q(k,s)] Q(k,s)
k=l s=l kl s=l


Small Number Emphasis is a measure of the fineness of the image. In a fine grain

image, the largest frequency of values will be in the small NGLDM numbers making

Q(k,s)/s2 larger. The reverse is true when the matrix element is multiplied by s2 rather

than divided. In this case, the Large Number Emphasis is a measure of the coarseness of

the image. Second Moment is a measure of homogeneity. In a homogeneous image, the Q

matrix will mostly be filled with small values, and, as a result, the NGLDM Second

Moment, which is the sum of squares of the entries, will be smaller. Number Non-

Uniformity and NGLDM Entropy are measures of the coarseness of an image determined

from the homogeneity of the number distribution in the Q matrix.









Univariate Classification Trials

Decision rules were created for each of the textures to test texture signatures

against the calibration images and validation images. These rules were used to classify

each of the images. To create the decision rules, kernel density signatures (Silverman

1986) were generated in S-Plus for each vegetation type combining data from all the

calibration images that contain a particular vegetation type. The number of output points

in the kernel density was set equal to the range of data so that ay value (density) would

be present for each integer x value. That output objective matches the integer values of

the texture images so that the probability that a particular image value belongs to a

particular vegetation class can be determined. The program DensityThreshold was used

to remove the tails of the kernel distributions from each vegetation class signature.

Because the x values output from S-plus denotes real values, the program also rounds to

the nearest integer. Rounding results in some missing integer values which the program,

extrapolating between neighboring values for a smooth density function, fills in. Finally,

all of the vegetation class signatures are combined in the Uclassify program to create a

decision vector. This vector is a simple list of the vegetation class that will be assigned to

a pixel when it has a given x value. The decision vector is created for each x value in the

range of observed x values by finding the highest density value (y value) among the

vegetation signatures and assigning that class to that x location. Figure 3-3 and Table 3-1

present an example of the procedure using the mean texture signatures. Once a decision

vector is created for a particular texture, it can be used repeatedly in the Uclassify

program for rapid classification of new texture images.














Mean Texture Signatures

0.25



0.2


S1. Schinus
--2. Palm
0.15 -- 3. Water
ft -*X--4. Mangrove
L -5. Herb
6. Shrub
0.1.- -j--- 7. Typha
S- 8. Myrica
9. Hardwood


0.05






DN



Figure 3-3. Combined signatures for the Mean Texture.







Table 3-1. Decision vector for the signatures shown in Figure 3-3.


Texture 85- 93- 108- 113- 122- 128- 135- 147- 156- 160- 182-
Value <85 92 107 112 121 126 127 134 145 146 155 159 181 191 >191

Class 0 3 0 9 7 6 7 6 8 5 4 5 1 2 0


Reference images (see Chapter 2) for each of the calibration and validation


images are used in the ErrorMatrix program to generate accuracy assessments of the


texture classifications. ErrorMatrix creates a contingency table (Congalton and Green


1999) of the distribution of classified pixels in relationship to the class of those pixels in


the reference image. The contingency table also reports user and producer accuracies by


class and overall accuracies. Producer accuracies are defined as the percent of area of a


specific class on the ground that is identified as that class on the map. User accuracies are









defined as the percent of areas called a specific class on the map that are that class on the

ground. The corollaries to user and producer accuracies are the omission and commission

errors. Omission error is when an area is excluded from the class to which it belongs.

Omission error is equal to 1 minus the producer's accuracy. Commission error, equal to 1

s accuracy, is when an area is included in an incorrect class. Figure 3-4

illustrates these definitions.




Ground Class A Classified as Class A






SI
S Agreement J '
Commission
Error




Ommission n1 .,. ii
Error



Figure 3-4. Illustration of accuracy assessment measures. In this example producer's
accuracy is high because most of the class has been correctly mapped and omission error
is low. User's accuracy is low because the mapped class includes a large area that is
misidentified and, therefore, commission error is high.









Results


Signature Development

Figures 3-5 through 3-7 show the distributions of the original gray level values for

each vegetation cover type in the infrared, red, and green bands. While the distribution of

Schinus gray levels values is generally higher than for the other species, the extreme

overlap between species distributions makes it impossible to distinguish one species from

another based solely on spectral values. The infrared band stretches the signatures apart

somewhat better than the red or green bands; however, the individual species remain

indistinguishable.

First-order statistical texture signatures for vegetation classes in the infrared of

image ww2042 are graphed for moving window neighborhoods ranging from 7 to 63

pixels in Figures 3-8 through 3-11. Texture signatures for green and red bands at various

window sizes were also graphed but have less separation than for the infrared and so are

not presented here. Separation of species resulting from sharper peaks in the species'

distributions, at least for the mean texture, improves with increasing window size. First-

order statistical textures of all the calibration image textures are graphed using the

infrared band at a moving window size of 63x63 pixels in Figures 3-12 through 3-17.

From all the 1st order textures in all the images, only the mean in the infrared band

and, to lesser extent, the standard deviation in the infrared band, showed any

discrimination of the vegetation types. In the three images containing Schinus, its range

for the mean texture was a consistent 150 to 200 (see Figure 3-12). Separation from the

range of values representing Schinus was excellent for Myrica, Typha, hardwoods, shrub

and, of course, water. Sabal and herbaceous separate in image ww2046 and both occur at









higher mean texture values than the Schinus found in other images. However, the Sabal

signature in ww1085 and the herbaceous and Avicennia signatures in ww1099 and

ww1093 are all within the same range as the Schinus signatures and will confuse with

Schinus in a classification. The standard deviation texture separates the herbaceous class

from Schinus with little overlap in both ww1093 and ww1099. Shrub also separates from

Schinus in ww1099. The other cover types do not separate at all. The skew texture does

not separate any of the cover types tested. Similarly, the entropy, homogeneity, and

Simpson's diversity textures do not separate any of the cover types except water.

Figure 3-18 graphs the Sun and Wee (1983) 2nd order or co-occurrence texture Q

matrix for vegetation classes in image ww2042 at moving window neighborhoods for the

P table of 3x3 and 7x7 and a inning of the data values to a maximum range of 64. There

is no effective discrimination between classes. The vegetation classes all fall in the lower

left-hand side of the Q matrix suggesting generally fine-textured classes with

approximately equal gray levels. Because the NGLDM textures are derived from the Q

matrix, they also show no discrimination of vegetation classes and are not presented here.


Univariate Calibration Image Classifications

The mean and the standard deviation textures were selected for classification

trials because they were the only textures to exhibit any discrimination between the

classes. Core areas of Schinus were discriminated when the mean texture was used as the

classifier for images ww2042 and ww1093 and core areas of Myrica discriminate in

ww2042 (Figure 3-19). In both images the classifications fail at the edges of the

vegetation types. Classification of the other calibration images with the mean texture was

completely unsuccessful. The accuracy assessments for the calibration images generally









show much higher user's accuracies than producer's accuracies (Tables 3-2 through 3-6).

This result is from the classifications correctly identifying vegetation types, but with

large omission errors. Image ww1085 is a good example. When hardwoods and Sabal are

identified, it is with 100% and 73% accuracy, respectively (user's accuracy); however,

hardwoods and Sabal are rarely identified, so the producer's accuracy is only 7% and

11% (Table 3-2). Image ww1093 has user accuracies of 73% and 100% for Schinus and

water and producer's accuracies of 54% for both. Avicennia and herbaceous cover types

both have low user and producer's accuracies, however (Table 3-3). Image ww1099 has

50% user's and producer's accuracy for Schinus and higher user's accuracies but low

producer's accuracies for herbaceous and shrub (Table 3-4). Schinus and water in image

ww2042 perform well (88% and 100% user's accuracy and 62% and 71% producer's

accuracy, respectively); however, Myrica confuses with Typha resulting in lower

accuracies for these types (Table 3-5). Table 3-6 shows that Sabal and herbaceous

classifications in image ww2046 resulted in poor accuracies.

Classification of the calibration images using the standard deviation texture

results in a very high level of confusion among classes (Figure 3-20). With the exception

of water, only hardwoods in image ww1085 show any indication of being successfully

identified. Even in the case of hardwoods, overall accuracy is very poor (Table 3-7).

While there is a 75% probability that a location identified as hardwood will be hardwood,

there is only a 10% probability that hardwoods will be identified. Water was identified

with 88% and 96% user's accuracy in images ww1093 (Table 3-8) and ww2042 (Table 3-

10), respectively. Producer's accuracies for water in those two images were 63% and









72% respectively. Accuracies for the other cover types were generally low (Tables 3-7

through 3-11).


Univariate Validation Image Classifications

When the mean and standard deviation textures where applied to the validation

images, the results showed no relationship to reality (Figures 3-21 and 3-22). In both

cases, the highest accuracies (excluding water) were for herbaceous ground cover in

image ww2008 (Tables 3-13 and 3-17). With user's accuracies of 87% for the mean

texture and 98% for the standard deviation texture, but producer's accuracies of only 12%

and 15%, herbaceous ground cover showed the same relationship of use and producer

accuracies that was seen for hardwoods using the standard deviation texture in ww1085.

That Schinus has a 93% user's accuracy in the standard deviation texture image 0212-528

(Table 3-16) is not meaningful since almost the entire image is Schinus; therefore,

anything that is classified as Schinus is correctly classified. More significant is the 4%

producer's accuracy that points to the large omission error in classification. It is not

surprising that Schinus in image 0212-528 was poorly discriminated by the mean and

standard deviation textures. The overexposed image has a direct effect on both measures.

Other cover types in the validation images have very low accuracies (Tables 3-12 through

3-19).




Discussion

Widely overlapping distributions of the original gray level values were expected

because of the high resolution of the imagery. A cover type has a mixture of distinct

spectral responses due to shadows, stems, species mixes, exposed ground, adjacent land









covers, and morphological variability. In larger (low-resolution) pixels, spectral

heterogeneity averages to characterize the overall spectral response of a class. In these

high-resolution images, the variance of spectral responses is not averaged and a broad

spectral distribution results.

For similar reasons, larger neighborhoods of analysis (analysis window sizes)

often discriminate between cover types better than small neighborhoods as was observed

in these analyses. The larger window accounts for more of the variance in the cover type.

The tradeoff is slower processing time and less precision in accurately classifying the

location of cover type edges.

In his trials with synthetic imagery, Weszka et al. (1976) reported that the simple

mean was the best discriminator among the first-order or second-order (Haralick textures)

statistics that he tried. That was also true for these images. While the mean gray level

value in a neighborhood often separated Schinus from other species in the imagery, it,

unfortunately, is not a gray level invariant texture and, therefore, is unlikely to be

consistent from image to image.

The co-occurrence matrices generated for these images showed no effective

separation of the cover types. The fine-grain versus coarse-grain distinctions that co-

occurrence textures emphasize may be valuable for separation of urban versus natural

cover types. However, the second-order gray-level variance about the natural cover type

images used here was not adequately different from one another. Sun and Wee (1983)

have applied these textures successfully to geologic features selected from Landsat-1

images. Taken by themselves, individual first and second-order textures do not






35


discriminate Schinus or the other tested cover types with adequate accuracy or

consistency.















ww1085


a,

a
9 -
0



0












a -



o
d





Co








o
0
0






d




cc.
o

(0

o -
o


0
d


ww1093


Sabal
Hardwoods












0 50 100 150 200 250

Infrared Band


ww1099


Schinus
Herbaceous
Shrub





It
/*,**




0 50 100 150 200 250

Infrared Band


ww2046


Sabal
Herbaceous












0 50 100 150 200 250

Infrared Band


a,








0-
C)








o
9 -
0
d
















0 -


0


Figure 3-5. Spectral signatures from the infrared image band.


ISchinus
Herbaceous
iAvicennia .......
Water .--.---
it








0 50 100 150 200 250

Infrared Band


ww2042


; Schinus
pl Myrica
i1 Typha ..
S Water --.---
iI
ii

I





0 50 100 150 200 250

Infrared Band















ww1085


a00






0
0







0
















a0
o .
0









o
0











0
0 .








0
o


ww1093


Sabal
Hardwoods











0 50 100 150 200 250

Red Band


ww1099


Schinus
Herbaceous
Shrub -----










0 50 100 150 200 250

Red Band


ww2046


Sabal
Herbaceous -








/


0 50 100 150 200 250

Red Band


00
0 -
0



0
o






0
0





0





9 -
0





0
o


Figure 3-6. Spectral signatures from the red image band.


iSchinus
iHerbaceous
iAvicennia ................
SWater .-----

lii







0 50 100 150 200 250

Red Band


ww2042


Schinus
Myrica
Typha ................
Water --.----

if\
i






0 50 100 150 200 250

Red Band















ww1085


aCO
9 -
0



o
0 .
0



0
0











0



0








CO
o
a>











0
0












0
00






*M
8


ww1093


Sabal
Hardwoods












0 50 100 150 200 250

Green Band


ww1099


Schinus
Herbaceous
Shrub -----




i\





0 50 100 150 200 250

Green Band


ww2046


Sabal
Herbaceous








I



0 50 100 150 200 250

Green Band


aO
0 -




SO

















9 -

a -
0




0

0

d


Figure 3-7. Spectral signatures from the green image band.


P Schinus
n Herbaceous
ii Avicennia .......
i Water .-----










0 50 100 150 200 250

Green Band


ww2042


Schinus
Myrica
Typha ................
S Water --.


is







0 50 100 150 200 250

Green Band















ww2042 7x7 neighborhood

Schinus
Myrica
Typha ---
Water -.----
I







50 100 150 200 250
Mean


ww2042 7x7 neighborhood

Schinus i
Myrica
Typha ..........
Water ----- -




I



110 115 120 125 130
Skew


ww2042 7x7 neighborhood


0 10 20 30 40 50
Homogeneity


ww2042 7x7 neighborhood

*! Schinus
I 1 Myrica
ti \Typha .......
i Water -------
I .'






0 10 20 30 40 50
Standard Deviation


ww2042 7x7 neighborhood

1 Schinus
i Myrica
\ Typha ..
j Water --.---








0 2 4 6 8 10
Entropy


ww2042 7x7 neighborhood

Schinus
Myrica
Typha
Water ----.


-" .\^ 1




140 160 180 200
Simpson Diversity


Figure 3-8. ww2042 spectral signatures of the first-order textures with a window size
7x7.













ww2042 15x15 neighborhood

Schinus
Myrica
i Typha ----
Water -------








50 100 150 200 250


Mean


ww2042 15x15 neighborhood

Schinus i
Myrica --
Typha .....
Water -------




t*



110 115 120 125 130
Skew


ww2042 15x15 neighborhood


Schinus
ft Myrica
ITypha ------------.
Water ....
I Water --------




: \\



0 10 20 30 40 50
Homogeneity


O -


ww2042 15x15 neighborhood

Schinus
Myrica
TTypha ---
SWater -- ---








0 10 20 30 40 50


Standard Deviation


ww2042 15x15 neighborhood

Schinus
Myrica
i Typha ..
Water -------
I i
i


r i__


0 2 4 6 8 10
Entropy


ww2042 15x15 neighborhood


Schinus
Myrica
Typha ..
Water ----



i /


.... . ........... .

140 160 180 200
Simpson Diversity


Figure 3-9. ww2042 spectral signatures of the first-order textures with a window size
15x15.













ww2042 31x31 neighborhood

Schinus
Myrica
Typha --
Water --.---

r5
i ft


I /0 1


50 100 150 200 250


Mean


ww2042 31x31 neighborhood

Schinus I
Myrica i
Typha -........... -
Water ------ i



1j I



110 115 120 125 130
Skew


ww2042 31x31 neighborhood

Schinus
Myric ---
Typh ------
r Water -------



f
Ii




0 10 20 30 40 50
Homogeneity


o -


ww2042 31x31 neighborhood


0 10 20 30 40 50


Standard Deviation


ww2042 31x31 neighborhood

S ;hinus -
i! M rica
i T ha
\ ter -----




I |


0 2 4 6 8 10
Entropy

ww2042 31x31 neighborhood

Schinus
Myrica --. -t
Typha .......
Water -----.
i 1



i I .



140 160 180 200
Simpson Diversity


Figure 3-10. ww2042 Spectral signatures of the first-order textures with a window size
31x31.













ww2042 63x63 neighborhood

S Schinus
i IVyrica
I %--yp h a - - ---. -
I A' ,/. >- . _. ... .


50 100 150 200 250
Mean


ww2042 63x63 neighborhood

Schinus t ill
Myrica ----
Typha -----
Water ----.-

I ii






110 115 120 125 130
Skew


ww2042 63x63 neighborhood


iII Schiriu~
Myrida
Typha ---------------
SWater .-------
lll I


I "I




0 10 20 30 40 50
Homogeneity


o -


ww2042 63x63 neighborhood

Schinus
Myrica
Typha
I Water --.---

'i1:



i / .'... ......

0 10 20 30 40 50

Standard Deviation


ww2042 63x63 neighborhood

i! S hinus
i! -rica
T ha ...
Vter --.-----






A 1_


0 2 4 6 8 10
Entropy


ww2042 63x63 neighborhood


Schinus
Myrica --
Typha ----
Water ----








140 160 180 200
Simpson Diversity


Figure 3-11. ww2042 Spectral signatures of the first-order textures with a window size
63x63.















ww1085


Sabal
Hardwoods












50 100 150 200 250

Mean


ww1099


50 100 150 200 250

Mean


ww2046


Sabal
Herbaceous



I

I





50 100 150 200 250

Mean


ww1093


cO
0


0
a,



0 .
0o


o


50 100 150 200 250

Mean


ww2042


0 -
0



O
0


0
O


50 100 150 200 250

Mean


Figure 3-12. Mean texture signatures.


cO
0
C,

o d
0



C)


1"q
HerbJ ous
i A ................


1 vi a
W a te ;c --. -. --


Schinus
jVlyrica
Sypha ---


00
0



0



0
o


c -


0 .


0


0
d
















(0

d
r



O
'>~

o O
O


0









(D

d
. O


'>~

, (


d
0
0


ww1093


ww1085


Sabal
Hardwoods











10 15 20 25 30 35 40

Standard Deviation


ww1099


Schinus
Shrub
Herb ---------






: I,
JI Ii






10 15 20 25 30 35 40

Standard Deviation


ww2046


Sabal
Herbaceous





,i


(D



>,6 O


0


d








(D
O





, (
d


10 20 30 40 50 60 70

Standard Deviation


Figure 3-13. Standard Deviation texture signatures.


1 Schinus
Avicennia

I Water .-----



hi S




10 15 20 25 30 35 40

Standard Deviation


ww2042


Schinus
Myrica
Typha .......
Water -------









10 15 20 25 30 35 40

Standard Deviation


(q


o




0
0

O













ww1085


Sabal
Hardwood s
S
I I







110 115 120 125 130 135 140


Skew


ww1099


er p ------------------
hru ----
erb







0
110 115 120 125 130 135 140
Skew


ww2046


SSabal
Herbaceous








0
o

110 115 120 125 130 135 140

Skew


Figure 3-14. Skew texture signatures.


ww1093


Schinusti
Avicenni
Herb ...
Water .-----








110 115 120 125 130 135 140


Skew


ww2042


110 115 120 125 130 135 140
Skew













ww1085


mo -


ww1093


mo -


Sabal
Hardwoods -









2 3 4 5 6
Entropy

ww1099

Sh inus
Slrub .
I rib ----------
~rb
i

I
I I




2 3 4 5 6
Entropy

ww2046

Sab i
Herc ous i


I iI
II I
I II

I


2 3 4 5 6
Entropy


Figure 3-15. Entropy texture signatures.


Schirus
Avicennia -
Herb
Water ...







2 3 4 5 6
Entropy

ww2042

Schinus
Myrica -
Typha ..............
Water --.----

I :I:

i :




2 3 4 5 6
Entropy















ww1085


ww1093


Sd
0







0
0


0 10 20 30 40


Homogeneity


ww1099


Schinus
Shrub
Herb










0 10 20 30 40

Homogeneity


0 10 20 30 40


Homogeneity


ww2042


00








0.


Schinus
Myrica
Typha .......
Water -------









0 10 20 30 40

Homogeneity


0 10 20 30 40

Homogeneity


Figure 3-16. Homogeneity texture signatures.


0

a>,




0
0


cO
0




o


d


ww2046


>,

s 5



0
0
0














ww1085


d
a, 5




Sd
o,


0


ww2046


d


a o
S


0
0


ww1093


d




o d


Sabal -
Hardwoods -



II
II





150 160 170 180 190 200

Simpson Diversity


ww1099


Schinus
Shrub
Herb .....-




Sii





150 160 170 180 190 200

Simpson Diversity


Schinus
Avicennia
Herb
Water .--.--






:_:


150 160 170 180 190 200

Simpson Diversity


ww2042


Schinus -
Myrica
Typha ----
Water -----.- I



'I




150 160 170 180 190 200

Simpson Diversity


150 160 170 180 190 200

Simpson Diversity


Figure 3-17. Simpson's Diversity texture signatures.


0
d

'>,



a, h
O4
o


0
0


,I





0


Sabal |
Herbaceous

II

II

I 1
t!



















w wD2 E cIhls Fwnw 3 QyrslW- 54
3lhiw
J- 1 'i f L


WWM42 kkr y Pyo i- 3 Q ebrw-&m4
AiM


*w. rW42pha pnndw-= 7 CWnd'= c 4
wil


yvwB342 7Wpa rnd h= 3 QCrdm= 64
wnc~-~pr Rc~sxu-- A V,~ 6


Figure 3-18. Normalized Gray Level Difference Matrix contour plots for vegetation

covers in image ww2042.


mww2D2 SchnnuaF PFrwdow 7 awr:w m B4
,.q(>


WWZN2 hIca fTwmfkw 7a nMOw 54


' "




























La.


c. ww1099


- SchiWnus terebinthifolius
S Sabal palmetto
-l Open Water
Avicennla germnnans
Herbaceous
Shrub
Typha spp.
I Myrica cerifera
I I Hardwoods


e. ww2U46


Figure 3-19. Classification of calibration images from the Mean texture.

















Table 3-2. Contingency table for ww1085 Mean textures classification.


Reference ww1085 Mean row produce
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 0 82 0 0 68 60 9 1 25 0 245 0.0%
Schinus 1 0 0 0 0 0 0 0 0 0 0 NA
Sabal 2 0 79 14 0 15 8 0 0 8 0 124 11.3%
Water 3 0 0 0 0 0 0 0 0 0 0 NA
Avicennia 4 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 0 0 0 0 0 0 0 0 0 0 NA
Shrub 6 0 0 0 0 0 0 0 0 0 0 NA
ll, 7 0 0 0 0 0 0 0 0 0 0 0 NA
Myrica 8 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 207 85 5 1 209 76 951 787 489 221 3031 7.3%

column count 207 246 19 1 292 144 960 788 522 221 340
User Accuracy 0.0% 0.0% 73.7% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%
Overall Accuracy 6.9%








Table 3-3. Contingency table for ww1093 Mean textures classification.


Reference ww1093 Mean row Producer
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 0 136 0 0 149 76 243 29 178 0 811 0.0%
Schinus 1 13 541 37 0 173 147 1 0 78 0 990 54.6%
Sabal 2 0 0 0 0 0 0 0 0 0 0 O NA
Water 3 144 0 0 257 0 0 15 32 3 24 475 54.1%
Avicennia 4 0 1 0 0 101 29 50 24 61 1 267 37.8%
Herbaceous 5 0 63 0 0 217 80 19 2 136 0 517 15.5%
Shrub 6 0 0 0 0 0 0 0 0 0 0 0 NA
il. 7 0 0 0 0 0 0 0 0 0 0 NA
Myrica 8 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 0 0 0 0 0 0 0 0 0 0 NA

column count 157 741 37 257 640 332 328 87 456 25 3060
UserAccuracy 0.0% 73.0% 0.0% 100.0% 15.8% 24.1% 0.0% 0.0% 0.0% 0.0%
Overall Accuracy 32.0%



















Table 3-4. Contingency table for ww1099 Mean textures classification.


Reference ww1099 Mean row Producer
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 0 17 1 0 21 10 16 0 42 0 107 0.0%
Schinus 1 128 389 89 0 74 63 12 0 39 0 794 49.0%
Sabal 2 0 0 0 0 0 0 0 0 0 0 NA
Water 3 0 0 0 0 0 0 0 0 0 0 NA
Avicennia 4 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 108 353 174 0 383 207 11 0 143 0 1379 15.0%
Shrub 6 0 6 4 0 66 19 153 19 148 0 415 36.9%
lil't 7 0 0 0 0 0 0 0 0 0 0 NA
Myrica 8 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 0 0 0 0 0 0 0 0 0 0 0.0%

column count 236 765 268 0 544 299 192 19 372 2695
User Accuracy 0.0% 50.8% 0.0% NA 0.0% 69.2% 79.7% 0.0% 0.0%
Overall Accuracy 27.8%








Table 3-5. Contingency table for ww2042 Mean textures classification.


Reference ww2042 Mean row Producer
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 2 21 0 0 32 13 22 5 44 5 144 1.4%
Schinus 1 0 573 66 0 115 96 14 7 51 0 922 62.1%
Sabal 2 0 15 2 0 2 2 0 0 0 0 21 9.5%
Water 3 111 0 0 377 0 0 6 18 0 19 531 71.0%
Avicennia 4 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 0 0 0 0 0 0 0 0 0 0 NA
Shrub 6 0 0 0 0 0 0 0 0 0 0 NA
i/l/.t 7 2 19 0 0 80 29 152 77 266 10 635 12.1%
Myrica 8 0 26 0 0 94 35 17 0 263 0 435 60.5%
Hardwood 9 0 0 0 0 0 0 0 0 0 0 NA

column count 115 654 68 377 323 175 211 107 624 34 2688
User Accuracy 1.7% 87.6% 2.9% 100.0% 0.0% 0.0% 0.0% 72.0% 42.1% 0.0%
Overall Accuracy 48.1%

















Table 3-6. Contingency table for ww2046 Mean textures classification.


Reference ww2046 Mean row Producel
0 1 2 3 4 5 6 7 8 9 Count Accuracy
UnClassified 0 249 276 112 1 118 54 88 37 107 7 1049 23.7%
Schinus 1 0 0 0 0 0 0 0 0 0 0 NA
Sabal 2 715 151 111 0 22 11 3 3 11 0 1027 10.8%
Water 3 0 0 0 0 0 0 0 0 0 0 NA
Avicennia 4 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 26 8 6 0 2 3 0 1 2 0 48 6.3%
Shrub 6 0 0 0 0 0 0 0 0 0 0 NA
,liph 7 0 0 0 0 0 0 0 0 0 0 NA
Myrica 8 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 0 0 0 0 0 0 0 0 0 0 NA

column count 990 435 229 1 142 68 91 41 120 7 2124
User Accuracy 25.2% 0.0% 48.5% 0.0% 0.0% 4.4% 0.0% 0.0% 0.0% 0.0%
Overall Accuracy 17.1%

























b. ww1093


C. WWIUYY


U. WWZU4Z


M Schinus terebinthifollus
- Sabal palmetto
M Open Water
Avicennia germinans
Herbaceous
Shrub
Typha spp.
M Myrica cerifera
SI Hardwoods


Figure 3-20. Classification of calibration images from the Standard Deviation texture.


e. ww2046

















Table 3-7. Contingency table for ww1085 Standard Deviation textures classification.


Reference ww1085 Standard Deviation row Producel
0 1 2 3 4 5 6 7 8 9 Count Accuracy
UnClassified 0 28 14 86 0 6 1 0 0 26 84 245 11.4%
Schinus 1 0 0 0 0 0 0 0 0 0 0 NA
Sabal 2 90 1 14 0 0 0 0 0 1 18 124 11.3%
Water 3 0 0 0 0 0 0 0 0 0 0 NA
Avicennia 4 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 0 0 0 0 0 0 0 0 0 0 NA
Shrub 6 0 0 0 0 0 0 0 0 0 0 NA
,lia 7 0 0 0 0 0 0 0 0 0 0 NA
Myrica 8 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 198 594 985 0 221 88 14 0 620 311 3031 10.3%

column count 316 609 1085 0 227 89 14 0 647 413 340
User Accuracy 8.9% 0.0% 1.3% NA 0.0% 0.0% 0.0% NA 0.0% 75.3%


Overall Accuracy


10.4%


Table 3-8. Contingency table for ww1093 Standard Deviation textures classification.


Reference ww1093 Standard Deviation row Producel
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 37 98 152 11 77 92 25 0 255 64 811 4.6%
Schnus 1 0 257 466 0 39 1 0 0 194 33 990 26.0%
Sabal 2 0 0 0 0 0 0 0 0 0 0 NA
Water 3 6 14 56 298 7 23 9 0 26 36 475 62.7%
Avicenna 4 9 46 115 0 10 0 0 0 32 55 267 3.7%
Herbaceous 5 2 86 60 28 43 101 59 0 123 15 517 19.5%
Shrub 6 0 0 0 0 0 0 0 0 0 0 NA
7 0 0 0 0 0 0 0 0 0 0 NA
8 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 0 0 0 0 0 0 0 0 0 0 NA

column count 54 501 849 337 176 217 93 0 630 203 306
UserAccuracy 68.5% 51.3% 0.0% 88.4% 5.7% 46.5% 0.0% NA 0.0% 0.0%
Overall Accuracy 23.0%



















Table 3-9. Contingency table for ww1099 Standard Deviation textures classification.


Reference ww1099 Standard Deviation row Producel
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 10 7 45 1 3 5 1 0 13 22 107 9.3%
Schinus 1 113 104 266 3 26 3 0 0 97 182 794 13.1%
Sabal 2 0 0 0 0 0 0 0 0 0 0 0 NA
Water 3 0 0 0 0 0 0 0 0 0 0 0 NA
Avicennia 4 0 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 39 131 257 222 86 254 156 0 141 93 1379 18.4%
Shrub 6 0 47 33 9 21 126 103 0 72 4 415 24.8%
Ilih 7 0 0 0 0 0 0 0 0 0 0 0 NA
Myrica 8 0 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 0 0 0 0 0 0 0 0 0 0 0 NA

column count 162 289 601 235 136 388 260 0 323 301 2695
User Accuracy 6.2% 36.0% 0.0% 0.0% 0.0% 65.5% 39.6% NA 0.0% 0.0% _


Overall Accuracy


17.5%


Table 3-10. Contingency table for ww2042 Standard Deviation textures classification.


Reference ww2042 Standard Deviation row Producel
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 2 35 57 0 4 0 0 0 29 17 144 1.4%
Schinus 1 17 257 344 0 60 0 0 0 186 58 922 27.9%
Sabal 2 0 2 11 0 0 0 0 0 0 8 21 52.4%
Water 3 1 20 45 384 10 15 18 0 13 25 531 72.3%
Avicennia 4 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 0 0 0 0 0 0 0 0 0 0 NA
Shrub 6 0 0 0 0 0 0 0 0 0 0 NA
/l'ih.t 7 21 124 136 15 49 53 44 0 150 43 635 0.0%
Myrica 8 0 120 125 0 40 26 1 0 123 0 435 28.3%
Hardwood 9 0 0 0 0 0 0 0 0 0 0 NA

column count 41 558 718 399 163 94 63 0 501 151 268
User Accuracy 4.9% 46.1% 1.5% 96.2% 0.0% 0.0% 0.0% NA 24.6% 0.0%
Overall Accuracy 28.9%

















Table 3-11. Contingency table for ww2046 Standard Deviation textures classification.


Reference ww2046 Standard Deviation row Producel
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 1016 1 7 0 1 1 0 0 1 22 1049 96.9%
Schinus 1 0 0 0 0 0 0 0 0 0 0 NA
Sabal 2 956 8 23 1 1 2 0 0 5 31 1027 2.2%
Water 3 0 0 0 0 0 0 0 0 0 0 NA
Avicennia 4 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 27 4 6 0 0 0 0 0 3 8 48 0.0%
Shrub 6 0 0 0 0 0 0 0 0 0 0 NA
,liph 7 0 0 0 0 0 0 0 0 0 0 NA
Myrica 8 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 0 0 0 0 0 0 0 0 0 0 NA

column count 1999 13 36 1 2 3 0 0 9 61 212
User Accuracy 50.8% 0.0% 63.9% 0.0% 0.0% 0.0% NA NA 0.0% 0.0% _
Overall Accuracy 48.9%






















b. ww2008


c. ww2060
c. ww2060


- Schinus terebinthifolus
- Sabal palmetto
- Open Water
Avicennla germfnans
Herbaceous
Shrub
Typha spp.
S Myria cerifera
I Hardwoods


Figure 3-21. Classification of validation images from the Mean texture.

















Table 3-12. Contingency table for 0212-528 Mean textures classification.


Reference 0212-528 Mean Texture Classification row Producer
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 791 156 80 0 37 24 0 0 8 0 1096 72.2%
Schinus 1 3400 88 93 0 6 7 0 0 0 0 3594 2.4%
Sabal 2 0 0 0 0 0 0 0 0 0 0 0 NA
Water 3 0 0 0 0 0 0 0 0 0 0 0 NA
Avicennia 4 0 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 0 0 0 0 0 0 0 0 0 0 0 NA
Shrub 6 0 0 0 0 0 0 0 0 0 0 0 NA

inl'.t 7 0 0 0 0 0 0 0 0 0 0 0 NA
Myrica 8 0 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 0 0 0 0 0 0 0 0 0 0 0 NA


column count 4191 244 173 0 43 31 0 0 8 0 4690
User Accuracy 18.9% 36.1% 0.0% NA 0.0% 0.0% NA NA 0.0% NA
Overall Accuracy 18.7%








Table 3-13. Contingency table for ww2008 Mean textures classification.


Reference ww2008 Mean Texture Classification row Producer
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 4 24 10 0 12 6 0 0 3 0 59 6.8%
Schinus 1 682 274 256 0 25 17 16 0 14 0 1284 21.3%
Sabal 2 0 0 0 0 0 0 0 0 0 0 0 NA
Water 3 0 0 0 0 0 0 0 0 0 0 0 NA
Avicennia 4 0 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 22 453 45 0 272 159 147 6 267 0 1371 11.6%
Shrub 6 0 0 0 0 0 0 0 0 0 0 0 NA
i/l/.t 7 0 0 0 0 0 0 0 0 0 0 0 NA
Myrica 8 0 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 0 0 0 0 0 0 0 0 0 0 0 NA


column count 708 751 311 0 309 182 163 6 284 0 2714
User Accuracy 0.6% 36.5% 0.0% NA 0.0% 87.4% 0.0% 0.0% 0.0% NA
Overall Accuracy 16.1%








60







Table 3-14. Contingency table for ww2060 Mean textures classification.


Reference ww2060 Mean Texture Classification row Producer
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 456 240 228 6 150 47 155 61 306 39 1688 27.0%
Schinus 1 180 51 20 0 17 13 3 0 11 0 295 17.3%
Sabal 2 0 0 0 0 0 0 0 0 0 0 0 NA
Water 3 960 2 0 87 7 4 109 111 30 61 1371 6.3%
Avicennia 4 0 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 0 0 0 0 0 0 0 0 0 0 0 NA
Shrub 6 0 0 0 0 0 0 0 0 0 0 0 NA
il't 7 0 0 0 0 0 0 0 0 0 0 0 NA
Myrica 8 0 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 0 0 0 0 0 0 0 0 0 0 0 NA


column count 1596 293 248 93 174 64 267 172 347 100 3354
User Accuracy 28.6% 17.4% 0.0% 93.5% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Overall Accuracy 17.7%








Table 3-15. Contingency table for ww2091 Mean textures classification.


Reference ww2091 Mean Texture Classification row Producer
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 122 327 105 9 75 40 52 28 97 10 865 14.1%
Schinus 1 0 0 0 0 0 0 0 0 0 0 0 NA
Sabal 2 1496 147 180 0 12 10 1 0 7 0 1853 9.7%
Water 3 469 14 1 19 25 13 126 337 61 421 1486 1.3%
Avicennia 4 0 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 0 0 0 0 0 0 0 0 0 0 0 NA
Shrub 6 0 0 0 0 0 0 0 0 0 0 0 NA
i/t 7 0 0 0 0 0 0 0 0 0 0 0 NA
Myrica 8 0 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 671 455 273 0 74 46 18 1 38 0 1576 0.0%


column count 2758 943 559 28 186 109 197 366 203 431 5780
User Accuracy 4.4% 0.0% 32.2% 67.9% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Overall Accuracy 5.6%



































d. ww2091


M Schinus terebinthifolius
- Sabal palmetto
M Open Water
Avicennla gerrmnans
Herbaceous
Shrub
Typha spp.
SMykrica cerifer
SI Hardwoods


Figure 3-22. Classification of validation images from the Standard Deviation texture.








62







Table 3-16. Contingency table for 0212-528 Standard Deviation textures classification.


Reference 0212-528 Standard Deviation Classification row Producer
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 982 10 40 1 1 1 2 0 10 49 1096 89.6%
Schinus 1 2252 129 414 58 48 65 60 0 151 417 3594 3.6%
Sabal 2 0 0 0 0 0 0 0 0 0 0 0 NA
Water 3 0 0 0 0 0 0 0 0 0 0 0 NA
Avicennia 4 0 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 0 0 0 0 0 0 0 0 0 0 0 NA
Shrub 6 0 0 0 0 0 0 0 0 0 0 0 NA
i/l. 7 0 0 0 0 0 0 0 0 0 0 0 NA
Myrica 8 0 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 0 0 0 0 0 0 0 0 0 0 0 NA


column count 3234 139 454 59 49 66 62 0 161 466 4690
User Accuracy 30.4% 92.8% 0.0% 0.0% 0.0% 0.0% 0.0% NA 0.0% 0.0%


Overall Accuracy


23.7%


Table 3-17. Contingency table for ww2008 Standard Deviation textures classification.


Reference ww2008 Standard Deviation Classification row Producer
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 9 6 22 0 6 0 1 0 7 8 59 15.3%
Schinus 1 278 155 539 10 22 4 0 0 90 186 1284 12.1%
Sabal 2 0 0 0 0 0 0 0 0 0 0 0 NA
Water 3 0 0 0 0 0 0 0 0 0 0 0 NA
Avicennia 4 0 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 142 139 183 277 62 199 111 0 167 91 1371 14.5%
Shrub 6 0 0 0 0 0 0 0 0 0 0 0 NA
i/l. 7 0 0 0 0 0 0 0 0 0 0 0 NA
Myrica 8 0 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 0 0 0 0 0 0 0 0 0 0 0 NA


column count 429 300 744 287 90 203 112 0 264 285 2714
User Accuracy 2.1% 51.7% 0.0% 0.0% 0.0% 98.0% 0.0% NA 0.0% 0.0%
Overall Accuracy 13.4%








63







Table 3-18. Contingency table for ww2060 Standard Deviation textures classification.


Reference ww2060 Standard Deviation Classification row Producer
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 377 122 286 39 64 221 162 0 220 197 1688 22.3%
Schinus 1 153 8 50 0 6 6 4 0 17 51 295 2.7%
Sabal 2 0 0 0 0 0 0 0 0 0 0 0 NA
Water 3 121 49 73 896 21 59 28 0 57 67 1371 65.4%
Avicennia 4 0 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 0 0 0 0 0 0 0 0 0 0 0 NA
Shrub 6 0 0 0 0 0 0 0 0 0 0 0 NA
il.t 7 0 0 0 0 0 0 0 0 0 0 0 NA
Myrica 8 0 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 0 0 0 0 0 0 0 0 0 0 0 NA


column count 651 179 409 935 91 286 194 0 294 315 3354
User Accuracy 57.9% 4.5% 0.0% 95.8% 0.0% 0.0% 0.0% NA 0.0% 0.0%


Overall Accuracy


38.2%


Table 3-19. Contingency table for ww2091 Standard Deviation textures classification.


Reference ww2091 Standard Deviation Classification row Producer
0 1 2 3 4 5 6 7 8 9 count Accuracy
UnClassified 0 748 8 28 4 3 1 1 0 13 59 865 86.5%
Schinus 1 0 0 0 0 0 0 0 0 0 0 0 NA
Sabal 2 1606 3 63 0 0 0 0 0 4 177 1853 3.4%
Water 3 172 17 41 1120 13 32 18 0 30 43 1486 75.4%
Avicennia 4 0 0 0 0 0 0 0 0 0 0 0 NA
Herbaceous 5 0 0 0 0 0 0 0 0 0 0 0 NA
Shrub 6 0 0 0 0 0 0 0 0 0 0 0 NA
il.t 7 0 0 0 0 0 0 0 0 0 0 0 NA
Myrica 8 0 0 0 0 0 0 0 0 0 0 0 NA
Hardwood 9 1228 11 108 0 0 0 0 0 13 216 1576 13.7%


column count 3754 39 240 1124 16 33 19 0 60 495 5780
User Accuracy 19.9% 0.0% 26.3% 99.6% 0.0% 0.0% 0.0% NA 0.0% 43.6%
Overall Accuracy 37.1%















CHAPTER 4
EDGE TEXTURES



Introduction

When considering visual discrimination, edges are often the first element that the

eye is drawn to in an image. Myler and Weeks (1993) asserted that edges contain most of

the information within an image. As much as color or gray-tone, edges define the

"texture" of an object by its structure. Edges occur at abrupt changes in image tone and so

are comprised of high-spatial-frequency information because spatial frequency increases

as spatial information increases per unit area. Hlavka (1987) found that one could

distinguish urban and rural areas with edge density textures applied to high altitude

scanner imagery.

In addition to density, edge has other properties that may be useful to texture

analysis as well. Edge strength or magnitude is a measure of how strongly the image tone

changes across the edge. Edge magnitude measures absolute difference. Another measure

is the rate of change across the edge--how abruptly the tonal change occurs. This last

measure can also be considered the magnitude of spatial frequency or change per unit

area.

This chapter considers textures measuring edge density, edge magnitude, and

edge rate of change from images generated by high-pass convolution filters. The

proportion of the images considered to be in the edge is also affected by a lower spectral

threshold value that will be examined.









Methods

Two edge filters were tested, the Laplacian filter and the Sobel filter. The

Laplacian filter enhances abrupt changes in intensity across the image and is non-

directional. The Sobel filter also enhances tonal edges but only in one direction,

horizontal or vertical, at a time (Myler and Weeks 1993). Passing a 3x3 filter over the

image and multiplying the filter values by the image values created Laplacian and Sobel

edge images. The filter values are shown in Figure 4-1. The Sobel filter requires two

passes, one for vertical components and one for horizontal components. The overall

Sobel edge is then computed as the square root of the sum of the squares of the vertical

edge and the horizontal edge.



.1 .1 1 2 1 -1 1
-1 8 -1 0 0 0 -2 0 2
-1 -1 -1 -1 -2 -1 -1 0 1

A B C

Figure 4-1. Values used in 3x3 edge filters. (A) Laplacian edge, (B) horizontal Sobel
edge, and (C) vertical Sobel edge.


To measure density, magnitude, and rate of change in edge pixel values, the

author created six image textures. Laplacian and Sobel edge images are produced as

intermediate edge images from the infrared band of the original images. The texture

images are then produced from characterization of the edge images. The six edge textures

are as follows:

1. Laplacian edge density. The relative density of edges in a moving window

from the Laplacian edge image. Relative density is simply the count of









pixel values above a chosen threshold divided by the total number of

pixels in the moving window. The relative density is multiplied by 100 to

work with integer results.

2. Sobel edge density. The relative density of edges in a moving window

from the Sobel edge image. Relative density is computed in the same

manner as the Laplacian edge density.

3. Median Sobel edge magnitude. The median value in a moving window of

the Sobel edge image.

4. Mean Sobel edge magnitude. The mean value in a moving window of the

Sobel edge image.

5. Sobel edge standard deviation. The standard deviation of the Sobel edge

image in a moving window.

6. Sobel Edge Slope. Computes the average rate of change in edge image

pixel values from the Sobel edge image in a moving window. Pixel value

differences are taken in the horizontal direction and in the vertical

direction and the maximum difference at each pixel in the moving window

is used in the calculation of rate of change. Rate of change is measured as

Z[max Ixy I ,1 I-I 1 V I N 100 where I is the spectral

intensity value of the Sobel edge image at coordinate pair x,y. and Nis the

total number of pixels in the moving window neighborhood.

A seventh texture, image standard deviation, is also included. Image standard

deviation is the standard deviation in a moving window of the original infrared image

values. Standard deviation is another approach to measuring edge strength. It is generally









expected that standard deviation will increase as a neighborhood window moves toward

an edge because the heterogeneity of pixel values increases. This texture is identical to

the standard deviation texture presented in Chapter 3 but is presented here for contrast to

the other edge detectors, particularly the Sobel edge standard deviation texture. The

Laplacian and Sobel edge images and the edge textures were created in the EdgeTextures

program listed in the Appendix.

Decision rules were created for each of the edge textures to test texture signatures

against the calibration and validation images. The decision rules were used to classify

each of the images. Creation of the decision rules, classification and accuracy assessment

proceeded as described in Chapter 3.



Results


Image Signatures

Figures 4-2 and 4-3 illustrate the effect of running a Sobel and Laplacian edge

filter on the calibration images and the visual effect of using a range of threshold values

on the image. The images were sensitive to the threshold value used. A threshold in the

20% range for the Sobel images and approximately 55% for the Laplacian images

seemed visually to give the best image definition. The Sobel images appeared visually to

have more range of edge responses and class separation than the Laplacian images.

Figure 4-4 illustrates the edge image histograms for image wwl085. The wwl085

histograms were characteristic of all the images. The Laplacian edge images had a sharp,

symmetrical peak centered on zero where the filter passes over an edge. It is this

characteristic that gave rise to a threshold near 50% (equal to a pixel value of zero)









displaying the best edge effect. The Sobel edge histogram, on the other hand, was

asymmetric and broader. The peak (mode) of the Sobel edge histogram again was

consistent with the threshold value that best displays edge differences. While the

Laplacian algorithm had a sharper response to edge, the broader histogram of the Sobel

edge allowed a measure of edge strength. It is for this reason that the Sobel edge was

used for all the textures that measure magnitude and variance.

Sensitivity tests of Sobel edge textures suggest that the most successful separation

of vegetation classes was with using a threshold of 10%. The Laplacian edge density

texture provided the most satisfactory results at a threshold of 55%. Sensitivity tests were

also conducted to select a suitable window size for analysis. As for the first order

statistical textures, the window size with the best class separation was the largest one

tested, 63 x 63 pixels. As the window size decreases, the variance of the signatures

increases.

Additionally, texture signatures were computed for both infrared and green bands

of each image. Only infrared band results are reported because they were consistently

superior to the green band results. The one exception was improved discrimination of

Typha in the green band of image ww2042.

Figure 4-5 graphs Laplacian Edge Density signatures for the calibration images

using the 55% threshold and the infrared band. As for the Sobel Edge Densities textures

(Figure 4-6), Laplacian Edge Densities discriminate Schinus and other vegetation types.

The Laplacian Edge Density did not have as good a separation of the signatures as the

Sobel Edge Density textures for some of the images and the Sabalpalmetto, herbaceous,

and hardwood classes did not discriminate at all in images ww1085 and ww2046.









However, herbaceous ground cover was more clearly separated in image ww1093 with

the Laplacian Edge Density than with the Sobel Edge Density. The absolute value of the

signatures for Laplacian Edge Density was not consistent among the images.

Figure 4-6 graphs the signatures of the Sobel Edge Density textures. Sabal

palmetto and hardwoods discriminate in image ww1085. Schinus, Avicennia, herbaceous

ground cover, and water discriminated in image ww1093, although considerable overlap

remains. Water was a single spike at zero and, thus, does not show up in the graph. In

image ww1099, the Sobel edge density texture provided separation for Schinus, shrub,

and herbaceous ground cover with some confusion between shrub and herbaceous

classes. Image ww2042 shows distinct distribution peaks for water, Myrica, and Schinus

with some confusion between Myrica and Schinus. Typha in image ww2042 confuses

with both Myrica and Schinus. The bi-modal distribution for Typha represents a mostly

water component of the signature and a mostly leaf surface component. Sabalpalmetto

and herbaceous ground cover separated for image ww2046. Across all the images

containing Schinus, it always had the highest densities of Sobel edge; however, the

absolute value of the signature range was not consistent from image to image.

Sobel Edge Magnitude was tested as a texture by recording the average and

median magnitude in a moving window, but only the average magnitude is presented

because the results were indistinguishable and the average requires less computation.

Figure 4-7 graphs all the calibration images with the Average Sobel Edge Magnitude

texture. The resulting signatures closely track the results of the Sobel Edge Density

textures (Figure 4-6).









Sobel Edge Slope texture (Figure 4-8) also had similar patterns of signature

discrimination to those seen in the Sobel Edge Densities texture (Figures 4-6). The range

of signature values for Schinus remained fairly stable with the mode falling above 2500

and below 3000 for all the images containing Schinus. In the Sobel Edge Slope textures

for image ww1093, the signatures for herbaceous ground cover and Avicennia do not

discriminate.

Sobel Edge Standard Deviation (Figures 4-9) differs from Image Standard

Deviation (Figure 4-10) in that it only measures the variance values of pixel that are

considered edge pixels. Sobel Edge Standard Deviation does a better job of

discriminating the vegetation classes than Image Standard Deviation. It, again, produced

almost identical, but noisier, signature appearance to the Sobel Edge Magnitude texture.

Sabal palmetto and herbaceous ground cover in image ww2046 did not separate,

however, as they did in the previous Sobel edge textures.

Figure 4-10 graphs the Image Standard Deviation signatures. The Image Standard

Deviation texture is the same algorithm as the first order Standard Deviation in Chapter

3. It is repeated here for contrast to the other edge detectors. Only water was completely

discriminated with this texture. However, as noted in Chapter 3, the herbaceous ground

cover was separated from Schinus in ww1093 and in image ww1099 both herbaceous

ground cover and shrub separated from Schinus, though not from each other.


Univariate Calibration Image Classifications

The Laplacian edge density texture classification shown in Figure 4-11 produced

both high user and producer accuracies in the discrimination of hardwoods in image

ww1085 (Table 4-1). This classification was also somewhat successful in discrimination









of Schinus with a tendency to overestimate its abundance, particularly in image wwl093.

Other vegetation types were poorly discriminated (Table 4-1 through Table 4-5).

The Sobel edge density texture classification (Figure 4-12 and Table 4-6 through

Table 4-10) produced poor results. Hardwoods (Table 4-6) and Schinus (Tables 4-7, 4-8,

and 4-9) both had high user's accuracies because when those classes are identified they

are mostly correct, but omission of large extents of Schinus and hardwoods result in low

producer's accuracies. Figure 4-12 illustrates the confusion when these classes are

mapped. Sabal and hardwoods were most likely to be confused with the Schinus class.

The Sobel edge magnitude classification preformed better in the identification of

Schinus. Accuracy Assessments of the images containing Schinus report a high user's

accuracy but low producer's accuracy (Tables 4-12, 4-13, and 4-14). Although user's and

producer's accuracies for Schinus are very similar to the Sobel edge density texture

classification results, the Sobel edge magnitude classification exhibits better cohesiveness

(Figure 4-13). That is, core areas of Schinus were generally well discriminated as

contiguous areas with most misidentification at the edge of the vegetation type. This is in

contrast to the Sobel edge density classification in which there was more

misclassification within the core areas of Schinus (Figure 4-12). Sabalpalmetto was most

likely to be confused with the Schinus class. Table 4-11 shows hardwoods with high

user's accuracy (97%), which is also similar to the results of the Sobel edge density

classification; however, the producer's accuracy is even lower (16% versus 29%). Water,

as is true with most of the textures, discriminates accurately. Other vegetation types in the

images were not discriminated (Tables 4-11 through 4-15).









Schinus had very similar user's accuracies when the Sobel edge slope texture is

used for classification (Tables 4-17, 4-18, and Table 4-19). Compared to the Sobel edge

magnitude classification, less of the core Schinus was detected (Figure 4-14) resulting in

10% to 20% lower producer's accuracies. Sabal and hardwoods were most likely to be

confused with the Schinus class. Hardwoods again have a 97% user's accuracy and a

considerably improved producer's accuracy of 54% (Table 4-16). The herbaceous class

also had a user's accuracy above 90% for image ww1099 (Table 4-18) but not in image

ww1093 (Table 4-17), and both images had a low producer's accuracy for herbaceous

ground cover.

The Sobel edge standard deviation texture classification (Figure 4-15)

discriminates Schinus with less accuracy than the other Sobel edge textures. User's

accuracies for images containing Schinus are 70%, 66%, and 89% (Tables 4-22, 4-23,

and 4-24) compared to 95%, 74%, and 94% for the same images classified with the Sobel

edge magnitude texture (Tables 4-12, 4-13, and 4-14). Producer's accuracies are about

the same. Again, Schinus was primarily confused with Sabalpalmetto. The Sobel edge

standard deviation texture classification accuracies for hardwoods (Table 4-21) and

herbaceous (Tables 4-22 and 4-23) are similar to the Sobel edge slope classification;

however, the hardwood class has a lower producer's accuracy.


Univariate Validation Image Classifications

Laplacian edge density classifications are not presented because Laplacian edges

were very uniformly distributed across the images, offering no discrimination of

vegetation classes. All of the classifications using decision rules from the calibration









images produced blank results. That is, all, or the majority, of the pixels in an image

classified to zero.

The Sobel edge density texture classification and the Sobel edge standard

deviation texture classification (Figure 4-16 and Figure 4-19; Table 4-26 through Table

4-29 and Table 4-38 through Table 4-41) had no success in identifying any of the

vegetation classes in the images. The Sobel edge density classification for image ww2091

had a 83% producer's accuracy for Sabalpalmetto; however, much of the correct

classification of Sabal results from a large commission error as seen in the 56% user's

accuracy (Table 4-29). With a 93% user's accuracy for image 0212-528 and 100% user's

accuracy in image ww2008, Sobel edge standard deviation appears to do well in the

identification of Schinus (Table 4-38 and Table 4-39); however, only 55 of 3594 pixels of

Schinus are identified in 0212-528 (1.5%) and the 100% user's accuracy in ww2008 is

from the identification of only 2 of the 1284 Schinus pixels in that image. The Sobel

magnitude texture and the Sobel edge slope texture classifications could only

successfully identify water (Figure 4-17 and Figure 4-18; Table 4-30 through Table 4-

37). The Sobel edge magnitude classification, like the Sobel edge standard deviation

classification, has high user's accuracy for Schinus in images 0212-528 and ww2008, but,

again, very few pixels of Schinus are actually identified and that high omission error is

reflected in the low producer's accuracies (Table 4-30 and Table 4-31).




Discussion

The edge texture classifiers showed promise for discrimination of vegetation

classes, particularly Schinus, when the signatures were observed for individual images.









Sobel edge textures are effective with all images but not always with the same threshold

(which can be very sensitive). Many of the Sobel edge textures gave similar, often good,

results, but there are specific differences in some textures getting better discrimination of

a specific vegetation class--thus the utility of a multivariate approach to be discussed in

Chapter 5.

However, when the signatures from the calibration images were combined to

generate an overall decision rule, the signature for any particular vegetation class

broadened to the extent that it no longer provided the separation needed to adequately

prevent confusion among the classes. Sobel edge density, Sobel edge magnitude, and

Sobel edge slope all had user's accuracies above 90% for Schinus in two of the three

calibration images containing Schinus (Table 4-7 through Table 4-9, Table 4-12 through

Table 4-14, and Table 4-17 through Table 4-19). Low producer's accuracies, however,

result in substantial omission.

Class confusion was particularly apparent when images not used in the calibration

were tested against the decision rules from the calibration images. Examination of the

Laplacian and Sobel edge images created from the validation images suggests that lower

edge thresholds may be required in many of these images to obtain satisfactory results.

If the signatures are developed in a supervised classification for individual

images, it is apparent that the edge textures should provide an excellent tool for

classification of the vegetation types tested. It was hoped, however, that the edge textures

would provide information relatively free from spectral variation among images and that

the edge textures among images would be reasonably constant. Those conditions should






75


allow unsupervised classification of vegetation classes based on the signatures of a few

calibration images. That was clearly not the case.























edges.










Threshold = 15%


Image ww2042, all edges.

0- i Aw~


Threshold = 25%










Threshold = 35%

Figure 4-2. Example Sobel edge images





















ww1093, all edges.


Threshold = 50%










Threshold = 55%










Threshold = 60%

Figure 4-3. Example Laplacian edge images.



















30000 -


25000-


20000 -


S15000 -


10000-


5000-


0-



b.


0 50 100 150 200 250
V1


-200 -100 0 100 200
V1


Figure 4-4. Sobel (a) and Laplacian (b) image histograms from edge images ofwwl085.


15000-





10000-

"












ww1085


Sabal -
Hardwoods
Iii


ill
I
I 1 L.


10 20 30 40 50
Laplacian Edge Density @ 55%

ww1099

Schinus
S Shrub
Herb ..............

i II





10 20 30 40 50
Laplacian Edge Density @ 55%

ww2046

Sabal
Herbaceous




" ll
1


10 20 30 40 50
Laplacian Edge Density @ 55%


ww1093


Schinus
Avicennia
Herb
Water -







10 20 30 40 50
Laplacian Edge Density @ 55%

ww2042

i j Schinus
i Myrica
ii i Typha ................
S Water ....-
iI!





10 20 30 40 50
Laplacian Edge Density @ 55%


Figure 4-5. Laplacian Edge Density texture signatures.














ww1085


Sabal
Hardwoods -




I I





0 20 40 60 80 100

Sobel Edge Density @ 10%


ww1099


0 20 40 60 80 100

Sobel Edge Density @ 10%


ww2046


Sabal
Herbaceous










0 20 40 60 80 100

Sobel Edge Density @ 10%


ww1093


(n



















(n
w,
0


Schinus
Avicennia
Herb ..
Water .-----



iihr




0 20 40 60 80 100

Sobel Edge Density @ 10%


ww2042


Schinus
Myrica
Typha .......
Water -----





"- .. . .." \ ",.


0 20 40 60 80 100

Sobel Edge Density @ 10%


Figure 4-6. Sobel Edge Density texture signatures


Schinus
Shrub
Herb ----






c..
. /' I "















ww1085


Sabal -
Hardqoot


'I
I




20 40 60 80
Sobel Edge Magnitude @ DN 10

wwl 099

Schinus
rub
rb c o s ----------------
I-frbaceous












ww2046
ii

I';




20 40 60 80
Sobel Edge Magnitude @ DN 10

ww2046

Sabal
Herbaceous





^Y. .I \K\


ww1093


Schinus
Avice r ia
Herb eous ................


.t i, - - - -




20 40 60 80
Sobel Edge Magnitude @ DN 10

ww2042

Schinus
Myrica
Typha ................
Water ---.--



-."". -:



20 40 60 80
Sobel Edge Magnitude @ DN 10


20 40 60 80
Sobel Edge Magnitude @ DN 10



Figure 4-7. Sobel Edge Magnitude texture signatures.













ww1085


Sabal
Hardwoods






/ \
./ \ M

1000 1500 2000 2500 3000 3500

Sobel Edge Slope @ 10%


ww1099

Schinus
Shrub
Herbaceous -------
Herbaceous

A
I1

:I



1000 1500 2000 2500 3000 3500

Sobel Edge Slope @ 10%


ww2046


Sabal
Herbaceous










1000 2000 3000 4000 5000
Sobel Edge Slope @10%


ww1093


Schinus
Avicennia
Herbaceous .......
Water .--.---




i I



1000 1500 2000 2500 3000 3500

Sobel Edge Slope @ 10%


ww2042

Schinus
Myrica
Typha .......
Water --.---
ii
i \




V..

1000 1500 2000 2500 3000 3500
Sobel Edge Slope @ 10%


Figure 4-8. Sobel Edge Slope texture signatures.














ww1085


0













0
,




,0
0 .(







O




O


(D
















(D
d















>,


o (M
0

O


ww1093


Sabal
Hardwoods ---






I., I

-LI

0 5 10 15 20 25 30

Sobel Edge Std. Dev.@ 10%


ww1099


Schinus
Shrub
Herbaceous -----
ii ,






... tj

0 5 10 15 20 25 30

Sobel Edge Std. Dev.@ 10%


ww2046


Sabal
Herbaceous











0 10 20 30 40 50 60

Sobel Edge Std. Dev.@ 10%


(D
0


>,
()





(0








O




(,
o (M


Figure 4-9. Sobel Edge Standard Deviation texture signatures.


Schinus
Avicennia -- n
Herbaceous a......
Water -

II ,


Li



0 5 10 15 20 25 30

Sobel Edge Std. Dev.@ 10%


ww2042


Schinus
Myrica
Typha .......
Water --.---




tl
i i







0 5 10 15 20 25 30

Sobel Edge Std. Dev.@ 10%














ww1085


(q

d



,0
O


O
O


ww1093


(D

O6



o C
O


O
0


Sabal
Hardwoods










10 15 20 25 30 35 40

Standard Deviation


ww1099


Schinus
Shrub
Herb ---------





I,
JI Ii





10 15 20 25 30 35 40

Standard Deviation


ww2046


Sabal
Herbaceous







i



10 20 30 40 50 60 70

Standard Deviation


1 Schinus
Avicennia

I Water .-----



hi f




10 15 20 25 30 35 40

Standard Deviation


ww2042


Schinus
Myrica
Typha .......
Water -------









10 15 20 25 30 35 40

Standard Deviation


Figure 4-10. Image Standard Deviation texture signatures.


(0



o
0





0
0,
d


d


(D
0




a
0


(q
d




,0


O
O
d




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