Change Detection and Image Time-Series Analysis 1 (eBook, PDF)

Unsupervised Methods
Redaktion: Atto, Abdourrahmane M.; Bruzzone, Lorenzo; Bovolo, Francesca

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 304
• Erscheinungstermin: 2. Dezember 2021
• Englisch
• ISBN-13: 9781119882244
• Artikelnr.: 63119266

Autorenporträt

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.

Inhaltsangabe

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 Moun