Change Detection and Image Time Series Analysis 2

Change Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series.

Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches.

Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns.

Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations,

Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.

Auteur
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.

Résumé
Change Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series.

Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches.

Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns.

Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations,

Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.

Contenu

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

List of Notations xiii

Chapter 1. Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series 1
*Ihsen HEDHLI, Gabriele MOSER, Sebastiano B. SERPICO and Josiane ZERUBIA*

1.1. Introduction 1

1.1.1. The role of multisensor data in time series classification 1

1.1.2. Multisensor and multiresolution classification 2

1.1.3. Previouswork 5

1.2. Methodology 9

1.2.1. Overview of the proposed approaches 9

1.2.2. Hierarchical model associated with the first proposed method 10

1.2.3. Hierarchical model associated with the second proposed method 13

1.2.4. Multisensor hierarchical MPM inference 14

1.2.5. Probability density estimation through finite mixtures 17

1.3. Examples of experimental results 19

1.3.1. Results of thefirstmethod 19

1.3.2. Results of the secondmethod 22

1.4. Conclusion 26

1.5. Acknowledgments 26

1.6. References 27

Chapter 2. Pixel-based Classification Techniques for Satellite Image Time Series 33
*Charlotte PELLETIER and Silvia VALERO*

2.1. Introduction 33

2.2. Basic concepts in supervised remote sensing classification 35

2.2.1. Preparing data before it is fed into classification algorithms 35

2.2.2. Key considerations when training supervised classifiers 39

2.2.3. Performance evaluation of supervised classifiers 41

2.3. Traditional classification algorithms 45

2.3.1. Support vector machines 45

2.3.2. Random forests 51

2.3.3. k-nearest neighbor 56

2.4. Classification strategies based on temporal feature representations 59

2.4.1. Phenology-based classification approaches 60

2.4.2. Dictionary-based classification approaches 61

2.4.3. Shapelet-based classification approaches 62

2.5. Deep learningapproaches 63

2.5.1. Introduction to deep learning 64

2.5.2. Convolutionalneuralnetworks 68

2.5.3. Recurrentneuralnetworks 71

2.6. References 75

Chapter 3. Semantic Analysis of Satellite Image Time Series 85
*Corneliu Octavian DUMITRU and Mihai DATCU*

3.1. Introduction 85

3.1.1.TypicalSITSexamples 89

3.1.2. Irregular acquisitions 90

3.1.3.The chapter structure 96

3.2.Why are semantics neededin SITS? 96

3.3.Similaritymetrics 97

3.4. Feature methods 98

3.5. Classification methods 98

3.5.1. Active learning 99

3.5.2. Relevance feedback 100

3.5.3. Compression-based pattern recognition 100

3.5.4. LatentDirichlet allocation 101

3.6. Conclusion 102

3.7. Acknowledgments 105

3.8. References 105

Chapter 4. Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond 109
*Matthieu MOLINIER, Jukka MIETTINEN, Dino IENCO, Shi QIU and Zhe ZHU*

4.1. Introduction 109

4.2. Annual time series 111

4.2.1. Overview of annual time series methods 111

4.2.2. Examples of annual times series analysis applications for environmentalmonitoring 112

4.2.3. Towardsdense time series analysis 116

4.3. Dense time series analysis using all available data 117

4.3.1. Making dense time series consistent 118

4.3.2. Change detection methods 121

4.3.3. Summaryand futuredevelopments 125

4.4. Deep learning-based time series analysis approaches 126

4.4.1. Recurrent Neural Network (RNN) for Satellite Image TimeSeries 129

4.4.2. Convolutional Neural Networks (CNN) for Satellite Image TimeSeries 131

4.4.3. Hybrid models: Convolutional Recurrent Neural Network (ConvRNN) models for Satellite Image Time Series 134 4.4.4. Synthesis and future deve...