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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 IGN] classification non dirigée [Termes IGN] classification pixellaire [Termes IGN] corrélation automatique de points homologues [Termes IGN] détection de changement [Termes IGN] Hypergraph Based Data Structure [Termes IGN] image radar moirée [Termes IGN] partition des données [Termes 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]

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A spatial anomaly points and regions detection method using multi-constrained graphs and local density / Yan Shi in Transactions in GIS, vol 21 n° 2 (April 2017)  

Public [article]  inTransactions in GIS > vol 21 n° 2 (April 2017) . - pp 376 – 405 Titre : A spatial anomaly points and regions detection method using multi-constrained graphs and local density Type de document : Article/Communication Auteurs : Yan Shi, Auteur ; Min Deng, Auteur ; Xuexi Yang, Auteur ; Qiliang Liu, Auteur Année de publication : 2017 Article en page(s) : pp 376 – 405 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse de données [Termes IGN] analyse spatiale [Termes IGN] attribut sémantique [Termes IGN] cartographie statistique [Termes IGN] détection d'anomalie [Termes IGN] graphe [Termes IGN] interpolation spatiale [Termes IGN] programmation par contraintes [Termes IGN] triangulation de Delaunay Résumé : (auteur) Spatial anomalies may be single points or small regions whose non-spatial attribute values are significantly inconsistent with those of their spatial neighborhoods. In this article, a Spatial Anomaly Points and Regions Detection method using multi-constrained graphs and local density (SAPRD for short) is proposed. The SAPRD algorithm first models spatial proximity relationships between spatial entities by constructing a Delaunay triangulation, the edges of which provide certain statistical characteristics. By considering the difference in non-spatial attributes of adjacent spatial entities, two levels of non-spatial attribute distance constraints are imposed to improve the proximity graph. This produces a series of sub-graphs, and those with very few entities are identified as candidate spatial anomalies. Moreover, the spatial anomaly degree of each entity is calculated based on the local density. A spatial interpolation surface of the spatial anomaly degree is generated using the inverse distance weight, and this is utilized to reveal potential spatial anomalies and reflect their whole areal distribution. Experiments on both simulated and real-life spatial databases demonstrate the effectiveness and practicability of the SAPRD algorithm. Numéro de notice : A2017-167 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12208 En ligne : http://dx.doi.org/10.1111/tgis.12208 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84701 [article]