Change Detection and Image Time-Series Analysis 1 Unervised Methods

by Atto, Abdourrahmane M.; Bovolo, Francesca; Bruzzone, Lorenzo

• ISBN13: 9781789450569
• ISBN10: 178945056X
• eBook ISBN(s): 9781119882251
• Edition: 1st
• Format: Hardcover
• Copyright: 2022-01-06
• Publisher: Wiley-ISTE
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Summary

Change Detection and Image Time Series Analysis 1 presents a wide range of unsupervised methods for temporal evolution analysis through the use of image time series associated with optical and/or synthetic aperture radar acquisition modalities.

Chapter 1 introduces two unsupervised approaches to multiple-change detection in bi-temporal multivariate images, with Chapters 2 and 3 addressing change detection in image time series in the context of the statistical analysis of covariance matrices. Chapter 4 focuses on wavelets and convolutional-neural filters for feature extraction and entropy-based anomaly detection, and Chapter 5 deals with a number of metrics such as cross correlation ratios and the Hausdorff distance for variational analysis of the state of snow. Chapter 6 presents a fractional dynamic stochastic field model for spatio temporal forecasting and for monitoring fast-moving meteorological events such as cyclones. Chapter 7 proposes an analysis based on characteristic points for texture modeling, in the context of graph theory, and Chapter 8 focuses on detecting new land cover types by classification-based change detection or feature/pixel based change detection. Chapter 9 focuses on the modeling of classes in the difference image and derives a multiclass model for this difference image in the context of change vector analysis.

Author Biography

Abdourrahmane M. Atto is Associate Professor at the University Savoie Mont Blanc, France. His research interests include mathematical methods and models for artificial intelligence and image time series.

Francesca Bovolo is the Head of the Remote Sensing for Digital Earth Unit, Fondazione Bruno Kessler, Italy. Her research interests include remote sensing image time series analysis, content-based time series retrieval and radar sounders.

Lorenzo Bruzzone is Professor of Telecommunications and the Founder and Director of the Remote Sensing Laboratory at the University of Trento, Italy. His research interests include remote sensing, machine learning and pattern recognition.

Table of Contents

Preface xi
Abdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE

List of Notations xv

Chapter 1. Unsupervised Change Detection in Multitemporal Remote Sensing Images 1
Sicong LIU, Francesca BOVOLO, Lorenzo BRUZZONE, Qian DU and Xiaohua TONG

1.1. Introduction 1

1.2. Unsupervised change detection in multispectral images 3

1.2.1.Related concepts 3

1.2.2.Open issues and challenges 7

1.2.3. Spectral–spatial unsupervised CD techniques 7

1.3. Unsupervised multiclass change detection approaches based on modelling spectral–spatial information 9

1.3.1. Sequential spectral change vector analysis (S2CVA) 9

1.3.2. Multiscale morphological compressed change vector analysis 11

1.3.3. Superpixel-level compressed change vector analysis 15

1.4.Dataset description and experimental setup 18

1.4.1.Dataset description 18

1.4.2.Experimental setup 22

1.5.Results anddiscussion 24

1.5.1.Results on the Xuzhou dataset 24

1.5.2. Results on the Indonesia tsunami dataset 24

1.6.Conclusion 28

1.7.Acknowledgements 29

1.8.References 29

Chapter 2. Change Detection in Time Series of Polarimetric SAR Images 35
Knut CONRADSEN, Henning SKRIVER, Morton J. CANTY and Allan A. NIELSEN

2.1. Introduction 35

2.1.1.The problem 36

2.1.2. Important concepts illustrated by means of the gamma distribution 39

2.2.Test theory and matrix ordering 45

2.2.1. Test for equality of two complex Wishart distributions 45

2.2.2. Test for equality of k-complex Wishart distributions 47

2.2.3. The block diagonal case 49

2.2.4.The Loewner order 52

2.3.The basic change detection algorithm 53

2.4.Applications 55

2.4.1.Visualizingchanges 58

2.4.2.Fieldwise change detection 59

2.4.3. Directional changes using the Loewner ordering 62

2.4.4. Software availability 65

2.5.References 70

Chapter 3. An Overview of Covariance-based Change Detection Methodologies in Multivariate SAR Image Time Series 73
Ammar MIAN, Guillaume GINOLHAC, Jean-Philippe OVARLEZ, Arnaud BRELOY and Frédéric PASCAL

3.1. Introduction 73

3.2.Dataset description 76

3.3.Statistical modelling of SAR images 77

3.3.1.The data 77

3.3.2.Gaussian model 77

3.3.3.Non-Gaussianmodeling 83

3.4.Dissimilarity measures 84

3.4.1.Problem formulation 84

3.4.2. Hypothesis testing statistics 85

3.4.3. Information-theoretic measures 87

3.4.4.Riemannian geometry distances 89

3.4.5.Optimal transport 90

3.4.6.Summary 91

3.4.7. Results of change detectors on the UAVSAR dataset 91

3.5. Change detection based on structured covariances 94

3.5.1. Low-rank Gaussian change detector 96

3.5.2. Low-rank compound Gaussian change detector 97

3.5.3. Results of low-rank change detectors on the UAVSAR dataset 100

3.6.Conclusion 102

3.7.References 103

Chapter 4. Unsupervised Functional Information Clustering in Extreme Environments from Filter Banks and Relative Entropy 109
Abdourrahmane M. ATTO, Fatima KARBOU, Sophie GIFFARD-ROISIN and Lionel BOMBRUN

4.1. Introduction 109

4.2.Parametric modelling of convnet features 110

4.3.Anomaly detection in image time series 113

4.4.Functional image time series clustering 119

4.5.Conclusion 123

4.6.References 123

Chapter 5. Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel-1 Image Time Series 127
Fatima KARBOU, Guillaume JAMES, Philippe DURAND and Abdourrahmane M. ATTO

5.1. Introduction 127

5.2.Test area and data 129

5.3.Wet snowdetectionusingSentinel-1 129

5.4.Metrics to detect wet snow 133

5.5.Discussion 138

5.6.Conclusion 143

5.7.Acknowledgements 143

5.8.References 143

Chapter 6. Fractional Field Image Time Series Modeling and Application to Cyclone Tracking 145
Abdourrahmane M. ATTO, Aluísio PINHEIRO, Guillaume GINOLHAC and Pedro MORETTIN

6.1. Introduction 145

6.2. Random field model of a cyclone texture 148

6.2.1.Cyclone texture feature 149

6.2.2.Wavelet-based power spectral densities and cyclone fields 150

6.2.3. Fractional spectral power decay model 153

6.3.Cyclonefield eye detection and tracking 157

6.3.1.Cyclone eye detection 157

6.3.2.Dynamic fractal field eye tracking 158

6.4. Cyclone field intensity evolution prediction 159

6.5.Discussion 161

6.6.Acknowledgements 163

6.7.References 163

Chapter 7. Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Images 167
Minh-Tan PHAM and Grégoire MERCIER

7.1. Introduction 167

7.2. Texture representation and characterization using local extrema 169

7.2.1.Motivation and approach 169

7.2.2. Local extrema keypoints within SAR images 172

7.3.Unsupervised change detection 175

7.3.1. Proposed framework 175

7.3.2.Weighted graph construction from keypoints 176

7.3.3.Change measure (CM) generation 178

7.4.Experimental study 179

7.4.1. Data description and evaluation criteria 179

7.4.2.Change detection results 181

7.4.3.Sensitivity to parameters 185

7.4.4.Comparison with the NLM model 188

7.4.5. Analysis of the algorithm complexity 191

7.5.Application to glacier flow measurement 192

7.5.1. Proposed method 193

7.5.2.Results 194

7.6.Conclusion 196

7.7.References 197

Chapter 8. Multitemporal Analysis of Sentinel-1/2 Images for Land Use Monitoring at Regional Scale 201
Andrea GARZELLI and Claudia ZOPPETTI

8.1. Introduction 201

8.2. Proposed method 203

8.2.1.Test site anddata 206

8.3.SAR processing 209

8.4.Optical processing 215

8.5.Combination layer 217

8.6.Results 219

8.7.Conclusion 220

8.8.References 221

Chapter 9. Statistical Difference Models for Change Detection in Multispectral Images 223
Massimo ZANETTI, Francesca BOVOLO and Lorenzo BRUZZONE

9.1. Introduction 223

9.2. Overview of the change detection problem 225

9.2.1. Change detection methods for multispectral images 227

9.2.2. Challenges addressed in this chapter 230

9.3. The Rayleigh–Rice mixture model for the magnitude of the difference image 231

9.3.1. Magnitude image statistical mixture model 231

9.3.2.Bayesian decision 233

9.3.3. Numerical approach to parameter estimation 234

9.4. A compound multiclass statistical model of the difference image 239

9.4.1. Difference image statistical mixture model 240

9.4.2. Magnitude image statistical mixture model 245

9.4.3.Bayesian decision 248

9.4.4. Numerical approach to parameter estimation 249

9.5.Experimental results 253

9.5.1.Dataset description 253

9.5.2.Experimental setup 256

9.5.3. Test 1: Two-class Rayleigh–Rice mixture model 256

9.5.4. Test 2: Multiclass Rician mixture model 260

9.6.Conclusion 266

9.7.References 267

List of Authors 275

Index 277

Summary of Volume 2 281