Change Detection and Image Time-Series Analysis 1: Unervised Methods

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.

Table of Contents:
Contents Preface xi Abdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE List of Notations Chapter 1 Unsupervised Change Detection in Multitemporal Remote Sensing Images 1 Sicong LIU, Francesca BOVOLO, Lorenzo BRUZZONE, QianDU and Xiaohua TONG 1.1. Introduction 1 1.2. Unsupervised change detection in multispectral images 3 1.2.1.Relatedconcepts 3 1.2.2.Openissuesandchallenges 7 1.2.3. Spectral–spatial unsupervised CD techniques 7 1.3 Unsupervised multiclass change detection approaches based on modelingspectral–spatialinformation 9 1.3.1 Sequential spectral change vector analysis (S 2 CVA) 9 1.3.2. Multiscale morphological compressed change vector analysis 11 1.3.3. Superpixel-level compressed change vector analysis 15 1.4.Datasetdescriptionandexperimentalsetup 18 1.4.1.Datasetdescription 18 1.4.2.Experimentalsetup 22 1.5.Resultsanddiscussion 24 1.5.1.ResultsontheXuzhoudataset 24 1.5.2. Results on the Indonesia tsunami dataset 24 xv 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, MortonJ.CANTY andAllanA.NIELSEN 2.1. Introduction 35 2.1.1.Theproblem 36 2.1.2 Important concepts illustrated by means of the gamma distribution 39 2.2.Testtheoryandmatrixordering 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.TheLoewnerorder 52 2.3.Thebasicchangedetectionalgorithm 53 2.4.Applications 55 2.4.1.Visualizingchanges 58 2.4.2.Fieldwisechangedetection 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.Datasetdescription 76 3.3.StatisticalmodelingofSARimages 77 3.3.1.Thedata 77 3.3.2.Gaussianmodel 77 3.3.3.Non-Gaussianmodeling 83 3.4.Dissimilaritymeasures 84 3.4.1.Problemformulation 84 3.4.2. Hypothesis testing statistics 85 3.4.3.Information-theoreticmeasures 87 3.4.4.Riemanniangeometrydistances 89 3.4.5.Optimaltransport 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.Parametricmodelingofconvnetfeatures 110 4.3.Anomalydetectioninimagetimeseries 113 4.4.Functionalimagetimeseriesclustering 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.Testareaanddata 129 5.3.WetsnowdetectionusingSentinel-1 129 5.4.Metricstodetectwetsnow 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.Cyclonetexturefeature 149 6.2.2. Wavelet-based power spectral densities and cyclone fields 150 6.2.3. Fractional spectral power decay model 153 6.3.Cyclonefieldeyedetectionandtracking 157 6.3.1.Cycloneeyedetection 157 6.3.2.Dynamicfractalfieldeyetracking 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.Motivationandapproach 169 7.2.2. Local extrema keypoints within SAR images 172 7.3.Unsupervisedchangedetection 175 7.3.1. Proposed framework 175 7.3.2. Weighted graph construction from keypoints 176 7.3.3.Changemeasure(CM)generation 178 7.4.Experimentalstudy 179 7.4.1. Data description and evaluation criteria 179 7.4.2.Changedetectionresults 181 7.4.3.Sensitivitytoparameters 185 7.4.4.ComparisonwiththeNLMmodel 188 7.4.5. Analysis of the algorithm complexity 191 7.5.Applicationtoglacierflowmeasurement 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.Testsiteanddata 206 8.3.SARprocessing 209 8.4.Opticalprocessing 215 8.5.Combinationlayer 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 differenceimage 231 9.3.1. Magnitude image statistical mixture model 231 9.3.2.Bayesiandecision 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.Bayesiandecision 248 9.4.4. Numerical approach to parameter estimation 249 9.5.Experimentalresults 253 9.5.1.Datasetdescription 253 9.5.2.Experimentalsetup 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

About the Author :
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.