Change Detection and Image Time-Series Analysis 1

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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. <br /><br />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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5 Preface

6 List of Notations

7 Begin Reading

8 List of Authors

9 Index

10 Summary of Volume 2

11 End User License Agreement

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SCIENCES

Image , Field Director – Laure Blanc-Feraud

Remote Sensing Imagery , Subject Heads – Emmanuel Trouvé and Avik Bhattacharya

Change Detection and Image Time Series Analysis 1

Unsupervised Methods

Coordinated by

Abdourrahmane M. Atto

Francesca Bovolo

Lorenzo Bruzzone

First published 2021 in Great Britain and the United States by ISTE Ltd and - фото 3

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: 2021941648

British Library Cataloguing-in-Publication Data

A CIP record for this book is available from the British Library

ISBN 978-1-78945-056-9

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

Preface

Abdourrahmane M. ATTO1, Francesca BOVOLO2 and Lorenzo BRUZZONE3

1University Savoie Mont Blanc, Annecy, France

2Fondazione Bruno Kessler, Trento, Italy

3University of Trento, Italy

This book is part of the ISTE-Wiley “ SCIENCES ” Encyclopedia and belongs to the Image field of the Engineering and Systems department. The Image field covers the entire processing chain from acquisition to interpretation by analyzing the data provided by various imaging systems. This field is split into seven subjects, including Remote Sensing Imagery (RSI). The heads of this subject are Emmanuel Trouvé and Avik Bhattacharya. In this subject, we propose a series of books that portray diverse and comprehensive topics in advanced remote-sensing images and their application for Earth Observation (EO). There has been an increasing demand for monitoring and predicting our planet’s evolution on a local, regional and global scale. Hence, over the past few decades, airborne, space-borne and ground-based platforms with active and passive sensors acquire images that measure several features at various spatial and temporal resolutions.

RSI has become a broad multidisciplinary domain attracting scientists across the diverse fields of science and engineering. The aim of the books proposed in this RSI series is to present the state-of-the-art and available scientific knowledge about the primary sources of images acquired by optical and radar sensors. The books cover the processing methods developed by the signal and image processing community to extract useful information for end-users for an extensive range of EO applications in natural resources.

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