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2. Deep Clustering Methods Study Applied to Satellite Images Time Series

Author

Lafabregue, Baptiste, Puissant, Anne, Weber, Jonathan, Forestier, Germain, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Image, Ville, Environnement (LIVE), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche en Informatique Mathématiques Automatique Signal - IRIMAS - UR 7499 (IRIMAS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), ANR-17-CE23-0015,TIMES,Exploitation de masses de données hétérogènes à haute fréquence temporelle pour l'analyse des changements environnementaux(2017), and Forestier, Germain

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], remote sensing, Deep learning, Image time-series, Clustering, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Clustering is an essential tool for data analysis and visualization. It is particularly useful in case of a lack of labels, which prevent the use of supervised methods. The analysis of satellite images is particularly prone to this problem, especially when studied as time series, because the access to this type of data is still recent. Among all clustering methods, the ones based on Deep Neural Networks (DNNs) have seen an increasing interest lately, but only a few works have been conducted on time series yet. This paper aims to give more insight on how current clustering methods based on DNNs can be applied to Satellite Images Time Series (SITS) and it shows that with a proper configuration they can perform better compared to classical non-deep methods.

Published
2022

3. Landslide Information System for Disaster Risk Financing: Earth Observation and Modelling Products for Near-Real- Time Assessment

Author

Michaud, Clement, primary, Malet, Jean-Philippe, additional, Oppikofer, Thierry, additional, Emberson, Robert, additional, Kirschbaum, Dalia, additional, Pacini, Fabrizio, additional, Horton, Pascal, additional, Puissant, Anne, additional, Mazzanti, Paolo, additional, Poteau, Melanie, additional, Oulidi, Abder, additional, Chaffai, Abderrahim, additional, and Brahim, Lahsen Ait, additional

Published
2021
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8. Spatio–Temporal Clustering and Active Learning for Change Classification in Satellite Image Time Series.

Author

Debonnaire, Nicolas, Stumpf, Andre, and Puissant, Anne

Abstract

Active learning (AL) has emerged as a versatile approach to reduce the training data required for remote sensing image classification, but its use for the analysis of satellite image time series (SITS) has not been explored yet. This study targets to explore a new object-based framework for change detection in SITS, combining the state-of-the-art spatio–temporal clustering and the use of different machine learning algorithms. Indeed, this study aims at testing whether standard machine learning algorithms can detect changes in long time series and whether AL can improve the results compared to traditional supervised learning. The tested AL algorithms comprise random forest-based heuristics that use a combination of uncertainty and diversity criteria, and a classical SVM breaking ties heuristic. The different implementations are evaluated with two datasets that depict changes around the Arcachon basin (West of France) and around the city of Colmar (East of France) spanning over more than 20 years and comprising four change classes (urban sprawl, forest gain, forest loss, and other). The tests demonstrate that steeper learning curves can be obtained with AL when compared to supervised learning. However, the performance of different AL algorithms depends on the dataset. [ABSTRACT FROM PUBLISHER]

Published
2016
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14. Active Learning in the Spatial Domain for Remote Sensing Image Classification.

Author

Stumpf, Andre, Lachiche, Nicolas, Malet, Jean-Philippe, Kerle, Norman, and Puissant, Anne

Subjects
REMOTE-sensing images, ACTIVE learning, STATISTICAL bootstrapping, SPATIAL distribution (Quantum optics), CLASSIFICATION algorithms
Abstract

Active learning (AL) algorithms have been proven useful in reducing the number of required training samples for remote sensing applications; however, most methods query samples pointwise without considering spatial constraints on their distribution. This may often lead to a spatially dispersed distribution of training points unfavorable for visual image interpretation or field surveys. The aim of this study is to develop region-based AL heuristics to guide user attention toward a limited number of compact spatial batches rather than distributed points. The proposed query functions are based on a tree ensemble classifier and combine criteria of sample uncertainty and diversity to select regions of interest. Class imbalance, which is inherent to many remote sensing applications, is addressed through stratified bootstrap sampling. Empirical tests of the proposed methods are performed with multitemporal and multisensor satellite images capturing, in particular, sites recently affected by large-scale landslide events. The assessment includes an experimental evaluation of the labeling time required by the user and the computational runtime, and a sensitivity analysis of the main algorithm parameters. Region-based heuristics that consider sample uncertainty and diversity are found to outperform pointwise sampling and region-based methods that consider only uncertainty. Reference landslide inventories from five different experts enable a detailed assessment of the spatial distribution of remaining errors and the uncertainty of the reference data. [ABSTRACT FROM AUTHOR]

Published
2014
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