Change Detection and Image Time-Series Analysis 1. Группа авторов

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      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 СКАЧАТЬ