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Contributions to SAR image time series analysis / Ammar Mian (2019)  

Public Titre : Contributions to SAR image time series analysis Type de document : Thèse/HDR Auteurs : Ammar Mian, Auteur ; Jean-Philippe Ovarlez, Directeur de thèse ; Guillaume Ginolhac, Directeur de thèse Editeur : Bures-sur-Yvette : Université Paris-Saclay Année de publication : 2019 Importance : 219 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l’Université Paris-Saclay préparée à Centrale-Supélec : Sciences et Technologies de l’Information et de la Communication Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] covariance [Termes IGN] détection de changement [Termes IGN] géométrie de Riemann [Termes IGN] image à très haute résolution [Termes IGN] image radar moirée [Termes IGN] ondelette de Shannon [Termes IGN] processus gaussien [Termes IGN] radar à antenne synthétique [Termes IGN] série temporelle [Termes IGN] transformation en ondelettes Index. décimale : THESE Thèses et HDR Résumé : (auteur) Remote sensing data from Synthetic Aperture Radar (SAR) sensors offer a unique opportunity to record, to analyze, and to predict the evolution of the Earth. In the last decade, numerous satellite remote sensing missions have been launched (Sentinel-1, UAVSAR, TerraSAR X, etc.). This resulted in a dramatic improvement in the Earth image acquisition capability and accessibility. The growing number of observation systems allows now to build high temporal/spatial-resolution Earth surface images data-sets. This new scenario significantly raises the interest in time-series processing to monitor changes occurring over large areas. However, developing new algorithms to process such a huge volume of data represents a current challenge. In this context, the present thesis aims at developing methodologies for change detection in high-resolution SAR image time series.These series raise two notable challenges that have to be overcome:On the one hand, standard statistical methods rely on multivariate data to infer a result which is often superior to a monovariate approach. Such multivariate data is however not always available when it concerns SAR images. To tackle this issue, new methodologies based on wavelet decomposition theory have been developed to fetch information based on the physical behavior of the scatterers present in the scene.On the other hand, the improvement in resolution obtained from the latest generation of sensors comes with an increased heterogeneity of the data obtained. For this setup, the standard Gaussian assumption used to develop classic change detection methodologies is no longer valid. As a consequence, new robust methodologies have been developed considering the family of elliptical distributions which have been shown to better fit the observed data.The association of both aspects has shown promising results in change detection applications. Note de contenu : Introduction 1- SAR Image Time Series issues 2- Wavelet packets for SAR analysis 3- Robust Change Detection 4- Change-point detection and estimation 5- Riemannian geometry Conclusion Numéro de notice : 25872 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences et Technologies de l’Information et de la Communication : Paris-Saclay : 2019 Organisme de stage : Laboratoire SONDRA nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02464840/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95547