Change Detection and Image Time Series Analysis 2

Supervised Methods

Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone

• Publisher: Wiley-ISTE
• Year: 2021
• Print ISBN: 9781789450576
• eBook ISBN: 9781119882275
• Edition: 1
• Topic: Informatica
• Subtopic: Visione artificiale e riconoscimento di schemi

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.

Table of contents

• Cover
• Half-Title Page
• Title Page
• Copyright Page
• Contents
• Preface
• List of Notations
• 1. Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series
• 2. Pixel-based Classification Techniques for Satellite Image Time Series
• 3. Semantic Analysis of Satellite Image Time Series
• 4. Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond
• 5. A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images
• 6. Multiclass Multilabel Change of State Transfer Learning from Image Time Series
• List of Authors
• Index
• Summary of Volume 1
• EULA