Change Detection and Image Time Series Analysis 2 E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Veröffentlichungsjahr: 2021
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Table of Contents
Cover
Title Page
Copyright
Preface
List of Notations
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
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
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
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
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
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
List of Authors
Index
Summary of Volume 1
End User License Agreement
Guide
Cover
Table of Contents
Title page
Copyright
Preface
List of Notations
Begin Reading
List of Authors
Index
Summary of Volume 1
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 2
Supervised Methods
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 Ltd27-37 St George’s RoadLondon SW19 4EUUK
www.iste.co.uk
John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA
www.wiley.com
© ISTE Ltd 2021The 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
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.
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.
Volume 1: Unsupervised methods
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. Chapters 2 and 3 introduce the concept of change detection in time series and postulate it in the context of statistical analysis of covariance matrices. The former chapter focuses on a directional analysis for multiple-change detection and exercises on a time series of SAR polarimetric data. The latter focuses on local analysis for binary change detection and proposes several covariance matrix estimators and their corresponding information-theoretic measures for multivariate SAR data. The last four chapters focus more on applications. Chapter 4 addresses functional representations (wavelets and convolutional neural network filters) for feature extraction in an unsupervised approach. It proposes anomaly detection and functional evolution clustering from this framework by using relative entropy information extracted from SAR data decomposition. Chapter 5 deals with the selection of metrics that are sensitive to snow state variation in the context of the cryosphere, with a focus on mountain areas. Metrics such as cross-correlation ratios and Hausdorff distance are analyzed with respect to optimal reference images to identify optimal thresholding strategies for the detection of wet snow by using Sentinel-1 image time series. Chapter 6 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.
Volume 2: Supervised methods
The second volume of this book is dedicated to supervised methods. Chapter 1 of this volume addresses the fusion of multisensor, multiresolution and multitemporal data. This chapter reviews recent advances in the literature and proposes two supervised Markov random field-based solutions: one relies on a quadtree and the second one 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. This chapter also includes best practices for reference data preparation and management, which are crucial tasks in supervised methods. Chapter 3 focuses on very high spatial resolution data time series and the use of semantic information for modeling spatio-temporal evolution patterns. Chapter 4 focuses 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 propose extensive evaluations of the methodologies used to produce earthquake-induced change maps, with an emphasis on their strengths and shortcomings (Chapter 5) and the deep learning systems in the context of multiclass multilabel change-of-state classification on glacier observations (Chapter 6).
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
List of Notations
Image Time Series: time index
k
and pixel position (
p, q)
Vector Image Time Series: band/spectral index
c
Matrix Image Time Series: (polarimetric indices (
u, v
))
Sets of Natural Numbers, Integers, Real and Complex Numbers
μ,
μ
Means of Random Variables and Random Vectors
C, Σ
Physical and Statistical Variance–Covariance Matrices
Probability Density Function
