Change Detection and Image Time Series Analysis 2

Supervised Methods


Change Detection and Image Time Series Analysis 2

Edited by

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

ISBN : 9781789450576

Publication Date : January 2022

Hardcover 266 pp

165.00 USD



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.


1. Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series, Ihsen Hedhli, Gabriele Moser, Sebastiano B. Serpico and Josiane Zerubia.
2. Pixel-based Classification Techniques for Satellite Image Time Series, Charlotte Pelletier and Silvia Valero.
3. Semantic Analysis of Satellite Image Time Series, Corneliu Octavian Dumitru and Mihai Datcu.
4. Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond, Matthieu Molinier, Jukka Miettinen, Dino Ienco, Shi Qiu and Zhe Zhu.
5. A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images, Gülsen Taskin, Esra Erten and Enes Oguzhan Alatas.
6. Multiclass Multilabel Change of State Transfer Learning from Image Time Series, Abdourrahmane M. Atto, Héla Hadhri, Flavien Vernier and Emmanuel Trouvé.

About the authors/editors

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.