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Convolutional neural networks for change analysis in earth observation images with noisy labels and domain shifts / Rodrigo Caye Daudt (2020)  

Public Titre : Convolutional neural networks for change analysis in earth observation images with noisy labels and domain shifts Type de document : Thèse/HDR Auteurs : Rodrigo Caye Daudt, Auteur ; Yann Gousseau, Directeur de thèse Editeur : Paris : Institut Polytechnique de Paris Année de publication : 2020 Importance : 151 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse de doctorat de l’Institut Polytechnique de Paris préparée à Telecom Paris, spécialité Informatique, données, intelligence artificielle Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse diachronique [Termes IGN] apprentissage dirigé [Termes IGN] apprentissage profond [Termes IGN] cartographie automatique [Termes IGN] classification par Perceptron multicouche [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de changement [Termes IGN] image multi sources [Termes IGN] réseau neuronal siamois [Termes IGN] segmentation sémantique [Termes IGN] vision par ordinateur Index. décimale : THESE Thèses et HDR Résumé : (Auteur) The analysis of satellite and aerial Earth observation images allows us to obtain precise information over large areas. A multitemporal analysis of such images is necessary to understand the evolution of such areas. In this thesis, convolutional neural networks are used to detect and understand changes using remote sensing images from various sources in supervised and weakly supervised settings. Siamese architectures are used to compare coregistered image pairs and to identify changed pixels. The proposed method is then extended into a multitask network architecture that is used to detect changes and perform land cover mapping simultaneously, which permits a semantic understanding of the detected changes. Then, classification filtering and a novel guided anisotropic diffusion algorithm are used to reduce the effect of biased label noise, which is a concern for automatically generated large-scale datasets. Weakly supervised learning is also achieved to perform pixel-level change detection using only image-level supervision through the usage of class activation maps and a novel spatial attention layer. Finally, a domain adaptation method based on adversarial training is proposed, which succeeds in projecting images from different domains into a common latent space where a given task can be performed. This method is tested not only for domain adaptation for change detection, but also for image classification and semantic segmentation, which proves its versatility. Note de contenu : 1. Introduction 1.1 Context 1.2 Domain 1.3 Objectives 1.4 Publications 2. Related Work 2.1 Computer Vision and Image Analysis 2.2 Machine Learning 2.3 Change Detection Using Remote Sensing Images 2.4 Evaluation Metrics 3. Supervised Change Detection 3.1 Introduction 3.2 ONERA Satellite Change Detection Dataset 3.3 Patch Based Architectures 3.4 Fully Convolutional Architectures 3.5 Experiments 3.6 Conclusion 4. Semantic Change Detection 62 4.1 High Resolution Semantic Change Detection Dataset 4.2 Methodology 4.3 Results 4.4 Conclusion 5. Weakly Supervised Change Detection 5.1 Change Detection with Unreliable Data 5.2 Method 5.3 Experiments 5.4 Analysis 5.5 Conclusion 6. Domain Adaptation for Change Detection 6.1 Motivation 6.2 Unsupervised Domain Adaptation 6.3 Formulation 6.4 Implementation 6.5 Results 6.6 Limitations and Discussion 6.7 Unpaired Translation of Change Detection Images 6.8 Conclusion 7. Conclusion Numéro de notice : 26557 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique, données, intelligence artificielle : Paris : 2020 Organisme de stage : Laboratoire Traitement et Communication de l'Information LTCI nature-HAL : Thèse DOI : sans Date de publication en ligne : 12/04/2021 En ligne : https://tel.archives-ouvertes.fr/tel-03105668/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98101