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Error-regulated multi-pass DInSAR analysis for landslide risk assessment / Jung Rack Kim in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 4 (April 2018)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 4 (April 2018) . - pp 189 - 202 Titre : Error-regulated multi-pass DInSAR analysis for landslide risk assessment Type de document : Article/Communication Auteurs : Jung Rack Kim, Auteur ; HyeWon Yun, Auteur ; Stephan van Gasselt, Auteur ; YunSoo Choi, Auteur Année de publication : 2018 Article en page(s) : pp 189 - 202 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Corée du sud [Termes IGN] effondrement de terrain [Termes IGN] interferométrie différentielle [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] modèle numérique de terrain [Termes IGN] montagne [Termes IGN] risque naturel [Termes IGN] surveillance géologique [Termes IGN] télédétection en hyperfréquence [Termes IGN] teneur en vapeur d'eau Résumé : (Auteur) Landslide risk assessment based on Differential Interferometric SAR analyses (DInSAR) is associated with a number of error effects. We here approach the problem of assessing landslide risks over mountainous areas, where DInSAR observations are often influenced by orographic effects and inaccurate base topographies by employing a dedicated error compensation. In order to obtain accurate information on surface deformation, we apply corrections for DInSAR interferograms using high-resolution base topography and water vapor information obtained from a satellite radiometer. We observe that the corrected DInSAR output is in accordance with the environmental context as inferred by geological and geomorphological settings. It is feasible to better quantify landslide monitoring schemes whenever high- accuracy atmospheric error maps and a methodology to effectively compensate for external errors in DInSAR interferograms are available. The approach in this study can be further upgraded for future SAR-based assessments and various error correction approaches for even more precise landslide risk assessments. Numéro de notice : A2018-138 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.4.189 Date de publication en ligne : 01/04/2018 En ligne : https://doi.org/10.14358/PERS.84.4.189 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89688 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2018041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Video event recognition and anomaly detection by combining gaussian process and hierarchical dirichlet process models / Michael Ying Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 4 (April 2018)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 4 (April 2018) . - pp 203 - 214 Titre : Video event recognition and anomaly detection by combining gaussian process and hierarchical dirichlet process models Type de document : Article/Communication Auteurs : Michael Ying Yang, Auteur ; Wentong Liao, Auteur ; Yanpeng Cao, Auteur ; Bodo Rosenhahn, Auteur Année de publication : 2018 Article en page(s) : pp 203 - 214 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] agent (intelligence artificielle) [Termes IGN] apprentissage non-dirigé [Termes IGN] approche hiérarchique [Termes IGN] image vidéo [Termes IGN] modèle de Markov [Termes IGN] modèle orienté agent [Termes IGN] séquence d'images Résumé : (Auteur) In this paper, we present an unsupervised learning framework for analyzing activities and interactions in surveillance videos. In our framework, three levels of video events are connected by Hierarchical Dirichlet Process (HDP) model: low-level visual features, simple atomic activities, and multi-agent interactions. Atomic activities are represented as distribution of low-level features, while complicated interactions are represented as distribution of atomic activities. This learning process is unsupervised. Given a training video sequence, low-level visual features are extracted based on optic flow and then clustered into different atomic activities and video clips are clustered into different interactions. The HDP model automatically decides the number of clusters, i.e., the categories of atomic activities and interactions. Based on the learned atomic activities and interactions, a training dataset is generated to train the Gaussian Process (GP) classifier. Then, the trained GP models work in newly captured video to classify interactions and detect abnormal events in real time. Furthermore, the temporal dependencies between video events learned by HDP-Hidden Markov Models (HMM) are effectively integrated into GP classifier to enhance the accuracy of the classification in newly captured videos. Our framework couples the benefits of the generative model (HDP) with the discriminant model (GP). We provide detailed experiments showing that our framework enjoys favorable performance in video event classification in real-time in a crowded traffic scene. Numéro de notice : A2018-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.4.203 Date de publication en ligne : 01/04/2018 En ligne : https://doi.org/10.14358/PERS.84.4.203 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89689 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2018041	RAB	Revue	Centre de documentation	En réserve L003	Disponible