Centre dedocumentation
scientifique

Unsupervised change detection between SAR images based on hypergraphs / Jun Wang in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 61 - 72 Titre : Unsupervised change detection between SAR images based on hypergraphs Type de document : Article/Communication Auteurs : Jun Wang, Auteur ; Xuexi Yang, Auteur ; Xiangyu Yang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 61 - 72 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] approche pixel [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] corrélation automatique de points homologues [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] Hypergraph Based Data Structure [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] partition des données [Termes descripteurs IGN] précision de la classification Résumé : (auteur) The performance of synthetic aperture radar (SAR) image change detection is mainly relied on the quality of the difference image and the accuracy of the classification method. Considering the above mentioned issues, this paper proposes an unsupervised framework for SAR image change detection in which each pixel is taken as a vertex and the collection of pixels is represented by hyperedges in a hypergraph. Thus, the task of SAR image change detection is formulated as the problem of hypergraph matching and hypergraph partition. First, instead of using the K nearest neighbour rule, we propose a coupling neighbourhood based on the spatial-intensity constraint to gather the neighbours for each vertex. Then, hyperedges are constructed on the pixels and their coupling neighbours. The weight of hyperedge is computed via the sum of the patch-based pairwise affinities within the hyperedge. Through modelling the two hypergraphs on the bi-temporal SAR images, not only the change level of vertices is described, but also the changes of local grouping and consistency within hyperedge are excavated. Thus, the difference image with a good separability can be obtained by matching each vertex and hyperedge between the two hypergraphs. Finally, a generalized hypergraph partition technique is employed to classify changed and unchanged areas in the generated difference image. Experimental results on real SAR datasets confirm the validity of the proposed framework in improving the robustness and accuracy of change detection. Numéro de notice : A2020-251 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.007 date de publication en ligne : 19/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94995 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020061	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2020063	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2020062	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt