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Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features / Wang Min in ISPRS Journal of photogrammetry and remote sensing, vol 123 (January 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 123 (January 2017) . - pp 104 - 113 Titre : Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features Type de document : Article/Communication Auteurs : Wang Min, Auteur ; Qi Cui, Auteur ; Wang Jie, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 104 - 113 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] agriculture [Termes IGN] analyse de données [Termes IGN] analyse multiéchelle [Termes IGN] aquaponie [Termes IGN] détection de cible [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à haute résolution [Termes IGN] segmentation d'image Résumé : (Auteur) In this paper, we first propose several novel concepts for object-based image analysis, which include line-based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance. Numéro de notice : A2017-010 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.10.008 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.10.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83911 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017011	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017013	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2017012	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

A region-line primitive association framework for object-based remote sensing image analysis / Wang Min in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 2 (February 2016) . - pp 149 - 159 Titre : A region-line primitive association framework for object-based remote sensing image analysis Type de document : Article/Communication Auteurs : Wang Min, Auteur ; Wang Jie, Auteur Année de publication : 2016 Article en page(s) : pp 149 - 159 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] primitive géométrique [Termes IGN] primitive topologique [Termes IGN] réseau routier [Termes IGN] segmentation d'image [Termes IGN] zone d'intérêt Résumé : (auteur) In this study, we propose a novel region-line primitive association framework (RLPAF) for OBIA. In this framework, segments (region primitive) and straight lines (line primitive) are obtained by image segmentation and straight line detection, respectively, before their corresponding intra-primitive features are extracted. An association model is built on inter-primitive topology and direction relationships. Several region-line collaborative features are also derived. Image analysis is then performed based on both region and line primitives. The advantage of RLPAF is the collaborative utilization of complementary information between regions and lines throughout the entire OBIA process: from image segmentation, to feature extraction, and finally, object recognition. To validate this framework, RLPAF is applied on road network extraction from high spatial resolution (HSR) remote sensing images. Experiments show that the proposed framework and methods refine primitive shape and spatial relationship analyses, as well as obtain higher method accuracy, than OBIAs based on only regions. Numéro de notice : A2016-056 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.2.149 En ligne : http://dx.doi.org/10.14358/PERS.82.2.149 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79657 [article]