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Lifting scheme-based sparse density feature extraction for remote sensing target detection / Ling Tian in Remote sensing, vol 13 n° 9 (May-1 2021)  

Public [article]  inRemote sensing > vol 13 n° 9 (May-1 2021) . - n° 1862 Titre : Lifting scheme-based sparse density feature extraction for remote sensing target detection Type de document : Article/Communication Auteurs : Ling Tian, Auteur ; Yu Cao, Auteur ; Zishan Shi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 1862 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de cible [Termes IGN] données clairsemées [Termes IGN] extraction de traits caractéristiques [Termes IGN] filtrage numérique d'image [Termes IGN] image optique [Termes IGN] image radar moirée [Termes IGN] transformation en ondelettes Résumé : (auteur) The design of backbones is of great significance for enhancing the location and classification precision in the remote sensing target detection task. Recently, various approaches have been proposed on altering the feature extraction density in the backbones to enlarge the receptive field, make features prominent, and reduce computational complexity, such as dilated convolution and deformable convolution. Among them, one of the most widely used methods is strided convolution, but it loses the information about adjacent feature points which leads to the omission of some useful features and the decrease of detection precision. This paper proposes a novel sparse density feature extraction method based on the relationship between the lifting scheme and convolution, which improves the detection precision while keeping the computational complexity almost the same as the strided convolution. Experimental results on remote sensing target detection indicate that our proposed method improves both detection performance and network efficiency. Numéro de notice : A2021-405 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13091862 Date de publication en ligne : 10/05/2021 En ligne : https://doi.org/10.3390/rs13091862 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97720 [article]