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Extraction from high-resolution remote sensing images based on multi-scale segmentation and case-based reasoning / Jun Xu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 3 (March 2022) . - pp 199 - 205 Titre : Extraction from high-resolution remote sensing images based on multi-scale segmentation and case-based reasoning Type de document : Article/Communication Auteurs : Jun Xu, Auteur ; Jiasong Li, Auteur ; Hao Peng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 199 - 205 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse d'image orientée objet [Termes IGN] classification barycentrique [Termes IGN] distance de Kullback-Leibler [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à haute résolution [Termes IGN] image Worldview [Termes IGN] masque [Termes IGN] occupation du sol [Termes IGN] segmentation d'image [Termes IGN] segmentation multi-échelle [Termes IGN] séparateur à vaste marge Résumé : (auteur) In object-oriented information extraction from high-resolution remote sensing images, the segmentation and classification of images involves considerable manual participation, which limits the development of automation and intelligence for these purposes. Based on the multi-scale segmentation strategy and case-based reasoning, a new method for extracting high-resolution remote sensing image information by fully using the image and nonimage features of the case object is proposed. Feature selection and weight learning are used to construct a multi-level and multi-layer case library model of surface cover classification reasoning. Combined with image mask technology, this method is applied to extract surface cover classification information from remote sensing images using different sensors, time, and regions. Finally, through evaluation of the extraction and recognition rates, the accuracy and effectiveness of this method was verified. Numéro de notice : A2022-202 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.20-00104R3 Date de publication en ligne : 01/03/2022 En ligne : https://doi.org/10.14358/PERS.20-00104R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100006 [article]

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