Change Detection and Image Time Series Analysis 1

Unsupervised Methods


Change Detection and Image Time Series Analysis 1

Edited by

Abdourrahmane M. Atto, University Savoie Mont Blanc, France
Francesca Bovolo, Digital Earth Unit - Fondazione Bruno Kessler, Italy
Lorenzo Bruzzone, Remote Sensing Laboratory - University of Trento, Italy

ISBN : 9781789450569

Publication Date : December 2021

Hardcover 296 pp

165.00 USD



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.

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.


1. Unsupervised Change Detection in Multitemporal Remote Sensing Images, Sicong Liu, Francesca Bovolo, Lorenzo Bruzzone, Qian Du and Xiaohua Tong.
2. Change Detection in Time Series of Polarimetric SAR Images, Knut Conradsen, Henning Skriver, Morton J. Canty and Allan A. Nielsen.
3. An Overview of Covariance-based Change Detection Methodologies in Multivariate SAR Image Time Series, Ammar Mian, Guillaume Ginolhac, Jean-Philippe Ovarlez, Arnaud Breloy and Frédéric Pascal.
4. Unsupervised Functional Information Clustering in Extreme Environments from Filter Banks and Relative Entropy, Abdourrahmane M. Atto, Fatima Karbou, Sophie Giffard-Roisin and Lionel Bombrun.
5. Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel-1 Image Time Series, Fatima Karbou, Guillaume James, Philippe Durand and Abdourrahmane M. Atto.
6. Fractional Field Image Time Series Modeling and Application to Cyclone Tracking, Abdourrahmane M. Atto, Aluísio Pinheiro, Guillaume Ginolhac and Pedro Morettin.
7. Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Images, Minh-Tan Pham and Grégoire Mercier.
8. Multitemporal Analysis of Sentinel-1/2 Images for Land Use Monitoring at Regional Scale, Andrea Garzelli and Claudia Zoppetti.
9. Statistical Difference Models for Change Detection in Multispectral Images, Massimo Zanetti, Francesca Bovolo and Lorenzo Bruzzone.

About the authors

Abdourrahmane M. Atto is Associate Professor at the University Savoie Mont Blanc, France. His research interests include mathematical methods and models for artificial intelligence and image time series.

Francesca Bovolo is the Head of the Remote Sensing for Digital Earth Unit, Fondazione Bruno Kessler, Italy. Her research interests include remote sensing image time series analysis, content-based time series retrieval and radar sounders.

Lorenzo Bruzzone is Professor of Telecommunications and the Founder and Director of the Remote Sensing Laboratory at the University of Trento, Italy. His research interests include remote sensing, machine learning and pattern recognition.