Abdourrahmane M. Atto - Change Detection and Image Time Series Analysis, Volume 2: Supervised Methods

SCIENCES

Image, Field Director Laure Blanc-Feraud

Remote Sensing Imagery, Subject Heads Emmanuel Trouv and Avik Bhattacharya

Coordinated by

Abdourrahmane M. Atto

Francesca Bovolo

Lorenzo Bruzzone

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 Georges 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

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 Trouv 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 planets 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.

In this project, each RSI book focuses on general topics such as change detection, surface displacement measurement, target detection, model inversion and data assimilation. This first book of the RSI series is dedicated to Change Detection and Image Time Series Analysis. It presents methods developed to detect changes and analyze their temporal evolutions using optical and/or synthetic aperture radar (SAR) images in diverse settings (e.g. image pairs, image time series). According to the numerous works and applications in this domain, this book is divided into two volumes, dedicated to unsupervised and supervised approaches, respectively. Unsupervised methods require little to no expert-based information to resolve a problem, whereas the contrary holds true, especially for methods that are supervised in the sense of providing a wide amount of labeled training data to the method, before testing this method.

A significant part of this book is dedicated to a wide range of unsupervised methods. The first chapter provides an insight into the motivations of this behavior and introduces two unsupervised approaches to multiple-change detection in bitemporal multispectral images. presents time series analysis in the context of spatio-temporal forecasting and monitoring fast-moving meteorological events such as cyclones. The application benefits from the fusion of remote sensing data under the fractional dynamic field assumption on the cyclone behavior. Chapter 7 proposes an analysis based on characteristic points for texture modeling with graph theory. Such an approach overcomes issues arising from large-size dense neighborhoods that affect spatial context-based approaches. The application proposed in this chapter concerns glacier flow measurement in bitemporal images. Chapter 8 focuses on detecting new land-cover types by classification-based change detection or feature/pixel-based change detection. Monitoring the construction of new buildings in urban and suburban scenarios at a large regional scale by means of Sentinel-1 and -2 images is considered as an application. Chapter 9 focuses on the statistical modeling of classes in the difference image and derives from scratch a multiclass model for it in the context of change vector analysis.

The second volume of this book is dedicated to supervised methods. ).

This book covers both methodological and application topics. From the methodological viewpoint, contributions are provided with respect to feature extraction and a large number of evaluation metrics for change detection, classification and forecasting issues. Analysis has been performed in both bitemporal images and time series, illustrating both unsupervised and supervised methods and considering both binary- and multiclass outputs. Several applications are mentioned in the chapters, including agriculture, urban areas and cryosphere analysis, among others. This book provides a deep insight into the evolution of change detection and time series analysis in the state-of-the-art, as well as an overview of the most recent developments.

July 2021

	Image Time Series: time index k and pixel position (p, q)
	Vector Image Time Series: band/spectral index c