Land Cover Classification of Remotely Sensed Images: A Textural Approach

ABSTRACT

i

ACKNOWLEDGEMENTS

iii

DEDICATION

v

TABLE OF CONTENTS

vi

LIST OF FIGURES

xi

LIST OF TABLES

xiv

LIST OF SYMBOLS AND ABBREVIATIONS

xvi

1

INTRODUCTION TO REMOTE SENSING

1

 

1.1

Basics of Remote Sensing

1

 

1.2

Resolution Characteristics of remotely sensed imagery data

7

 

1.3

Reflectance Characteristics of Remotely Sensed Imagery

9

 

1.4

Remote sensing applications

12

 

1.5

Types of remotely sensed images

 

2

INTRODUCTION TO TEXTURE

14

 

2.1

Basics of texture

14

 

2.2

Texture analysis

 

3

LITERATURE SURVEY

19

 

3.1

Introduction

19

 

3.2

Survey Papers on Texture Models

19

 

3.3

Texture Models used for Characterization of Images

26

 

 

3.3.1

Structural Texture Models

27

 

 

3.3.2

Statistical Texture Models

27

 

 

3.3.3

Spectral Models

30

 

 

3.3.4

Model based Texture Models

30

 

 

3.3.5

Fuzzy based Models

31

 

 

3.3.6

Combined (texture and colour) approach Models

 

 

3.4

Classifiers applied in texture based study

42

 

3.5

Distance measures in texture based study

45

4

A FEW EXISTING BASIC AND MULTIVARIATE TEXTURE MODELS

49

 

4.1

Multivariate Local Binary Pattern

49

 

4.2

Multivariate Local Texture Pattern

50

 

4.3

Gray Level Co-occurrence Matrix

51

 

4.4

Texture Spectrum

54

 

4.5

Discrete Local Texture Pattern

 

 

4.6

Local Derivative Pattern

 

 

4.7

MATLAB codes of basic texture models

 

5

TEXTURE BASED SEGMENTATION USING BASIC TEXTURE MODELS

77

 

5.1

Texture based classification

77

 

5.2

Texture based segmentation

78

 

5.3

k-Nearest Neighbour (k-NN) Classifier

 

 

5.4

Experimental data

 

 

5.5

Matlab codes for texture based segmentation

 

 

 

5.5.1

GLCM and minimum distance classifier

 

 

 

5.5.2

LBP and minimum distance classifier

 

6

TEXTURE BASED SEGMENTATION USING LBP WITH SUPERVISED AND UNSUPERVISED CLASSIFIERS

 

 

6.1

Texture Segmentation using LBP with Supervised Classifiers

78

 

 

6.1.1

LBP with fuzzy k-NN

 

 

 

6.1.2

LBP with SVM

 

 

 

6.1.3

LBP with ANFIS

 

 

 

6.1.4

LBP with ELM

 

 

 

6.1.5

LBP with HMM

 

 

6.2

Texture Segmentation using LBP with Unsupervised Classifiers

 

 

 

6.2.1

LBP with SOM

 

 

 

6.2.2

LBP with FCM

 

7

TEXTURE BASED CLASSIFICATION OF REMOTELY SENSED IMAGES

 

 

7.1

Issues and challenges in texture based classification of remotely sensed images

 

 

7.2

The proposed texture model

 

 

7.3

Matlab code : Classification Procedure for texture based classification of remotely sensed images using the proposed texture model

 

 

7.4

The proposed approach using HMM

 

8

PERFORMANCE METRICS

135

REFERENCES

 

LIST OF PUBLICATIONS BY AUTHOR

 

AUTHOR'S BIOGRAPHY