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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]

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