Change Detection and Image Time Series Analysis 2

1 Cover

2 Title Page SCIENCES Image , Field Director – Laure Blanc-Feraud Remote Sensing Imagery , Subject Heads – Emmanuel Trouvé and Avik Bhattacharya

3 Copyright First published 2021 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc. Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address: ISTE Ltd 27-37 St George’s Road London SW19 4EU UK www.iste.co.uk John Wiley & Sons, Inc. 111 River Street Hoboken, NJ 07030 USA www.wiley.com © ISTE Ltd 2021 The rights of Abdourrahmane M. Atto, Francesca Bovolo and Lorenzo Bruzzone to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988. Library of Congress Control Number: 2021941720 British Library Cataloguing-in-Publication Data A CIP record for this book is available from the British Library ISBN 978-1-78945-057-6 ERC code: PE1 Mathematics PE1_18 Scientific computing and data processing PE10 Earth System Science PE10_3 Climatology and climate change PE10_4 Terrestrial ecology, land cover change PE10_14 Earth observations from space/remote sensing

4 Preface

5 List of Notations

6 1 Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series 1.1. Introduction 1.2. Methodology 1.3. Examples of experimental results 1.4. Conclusion 1.5. Acknowledgments 1.6. References

7 2 Pixel-based Classification Techniques for Satellite Image Time Series 2.1. Introduction 2.2. Basic concepts in supervised remote sensing classification 2.3. Traditional classification algorithms 2.4. Classification strategies based on temporal feature representations 2.5. Deep learningapproaches 2.6. References

8 3 Semantic Analysis of Satellite Image Time Series 3.1. Introduction 3.2. Why are semantics neededin SITS? 3.3. Similaritymetrics 3.4. Feature methods 3.5. Classification methods 3.6. Conclusion 3.7. Acknowledgments 3.8. References

9 4 Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond 4.1. Introduction 4.2. Annual time series 4.3. Dense time series analysis using all available data 4.4. Deep learning-based time series analysis approaches 4.5. Beyond satellite image time series and deep learning: convergence between time series and video approaches 4.6. References

10 5 A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images 5.1. Introduction 5.2. Satellite-based earthquake damage assessment 5.3. Pre-processing of satellite images before damage assessment 5.4. Multi-source image analysis 5.5. Contextual feature mining for damage assessment 5.6. Multi-temporal image analysis for damage assessment 5.7. Understanding damage following an earthquake using satellite-based SAR 5.8. Use of auxiliary data sources 5.9. Damage grades 5.10. Conclusionand discussion 5.11. References

11 6 Multiclass Multilabel Change of State Transfer Learning from Image Time Series 6.1. Introduction 6.2. Coarse- to fine-grained change of state dataset 6.3. Deep transfer learning models for change of state classification 6.4. Change of state analysis 6.5. Conclusion 6.6. Acknowledgments 6.7. References

12 List of Authors

13 Index

14 Summary of Volume 1

15 End User License Agreement

1 Cover

2 Table of Contents

3 Title page

4 Copyright First published 2021 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc. Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address: ISTE Ltd 27-37 St George’s Road London SW19 4EU UK www.iste.co.uk John Wiley & Sons, Inc. 111 River Street Hoboken, NJ 07030 USA www.wiley.com © ISTE Ltd 2021 The rights of Abdourrahmane M. Atto, Francesca Bovolo and Lorenzo Bruzzone to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988. Library of Congress Control Number: 2021941720 British Library Cataloguing-in-Publication Data A CIP record for this book is available from the British Library ISBN 978-1-78945-057-6 ERC code: PE1 Mathematics PE1_18 Scientific computing and data processing PE10 Earth System Science PE10_3 Climatology and climate change PE10_4 Terrestrial ecology, land cover change PE10_14 Earth observations from space/remote sensing

5 Preface

6 List of Notations

7 Begin Reading

8 List of Authors

9 Index

10 Summary of Volume 1

11 End User License Agreement

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