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On the spatial distribution of buildings for map generalization / Zhiwei Wei in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)  

Public [article]  inCartography and Geographic Information Science > Vol 45 n° 6 (November 2018) . - pp 539 - 555 Titre : On the spatial distribution of buildings for map generalization Type de document : Article/Communication Auteurs : Zhiwei Wei, Auteur ; Qingsheng Guo, Auteur ; Lin Wang, Auteur ; Fen Yan, Auteur Année de publication : 2018 Article en page(s) : pp 539 - 555 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement [Termes IGN] analyse en composantes principales [Termes IGN] arbre aléatoire minimum [Termes IGN] bati [Termes IGN] distribution spatiale [Termes IGN] données localisées des bénévoles [Termes IGN] généralisation cartographique automatisée [Termes IGN] OpenStreetMap [Vedettes matières IGN] Généralisation Résumé : (Auteur) Information on spatial distribution of buildings must be explored as part of the process of map generalization. A new approach is proposed in this article, which combines building classification and clustering to enable the detection of class differences within a pattern, as well as patterns within a class. To do this, an analysis of existing parameters describing building characteristics is performed via principal component analysis (PCA), and four major parameters (i.e. convex hull area, IPQ compactness, number of edges, and smallest minimum bounding rectangle orientation) are selected for further classification based on similarities between building characteristics. A building clustering method based on minimum spanning tree (MST) considering rivers and roads is then applied. Theory and experiments show that use of a relative neighbor graph (RNG) is more effective in detecting linear building patterns than either a nearest neighbor graph (NNG), an MST, or a Gabriel graph (GssG). Building classification and clustering are therefore conducted separately using experimental data extracted from OpenStreetMap (OSM), and linear patterns are then recognized within resultant clusters. Experimental results show that the approach proposed in this article is both reasonable and efficient for mining information on the spatial distribution of buildings for map generalization. Numéro de notice : A2018-480 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2018.1433068 Date de publication en ligne : 15/02/2018 En ligne : https://doi.org/10.1080/15230406.2018.1433068 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91258 [article]

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Band subset selection for anomaly detection in hyperspectral imagery / Lin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 4887 - 4898 Titre : Band subset selection for anomaly detection in hyperspectral imagery Type de document : Article/Communication Auteurs : Lin Wang, Auteur ; Chein-I Chang, Auteur ; Li-Chien Lee, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 4887 - 4898 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] détection d'anomalie [Termes IGN] détection de cible [Termes IGN] image hyperspectrale [Termes IGN] jeu de données Résumé : (Auteur) This paper presents a new approach, called band subset selection (BSS)-based hyperspectral anomaly detection (AD), which selects multiple bands simultaneously as a band subset rather than selecting multiple bands one at a time as the tradition band selection (BS) does, referred to as sequential multiple BS (SQMBS). Its idea is to first use virtual dimensionality (VD) to determine the number of multiple bands, nBS needed to be selected as a band subset and then develop two iterative process, sequential BSS (SQ-BSS) algorithm and successive BSS (SC-BSS) algorithm to find an optimal band subset numerically among all possible nBS combinations out of the full band set. In order to terminate the search process the averaged least-squares error (ALSE) and 3-D receiver operating characteristic (3D ROC) curves are used as stopping criteria to evaluate performance relative to AD using the full band set. Experimental results demonstrate that BSS generally performs better background suppression while maintaining target detection capability compared to target detection using full band information. Numéro de notice : A2017-658 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2681278 En ligne : https://doi.org/10.1109/TGRS.2017.2681278 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87069 [article]