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Auteur C.L. Philip Chen |
Documents disponibles écrits par cet auteur (2)
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Region-kernel-based support vector machines for hyperspectral image classification / Jiangtao Peng in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)
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Titre : Region-kernel-based support vector machines for hyperspectral image classification Type de document : Article/Communication Auteurs : Jiangtao Peng, Auteur ; C.L. Philip Chen, Auteur ; Yicong Zhou, Auteur Année de publication : 2015 Article en page(s) : pp 4810 - 4824 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification barycentrique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] fonction régionalisée
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyauRésumé : (Auteur) This paper proposes a region kernel to measure the region-to-region distance similarity for hyperspectral image (HSI) classification. The region kernel is designed to be a linear combination of multiscale box kernels, which can handle the HSI regions with arbitrary shape and size. Integrating labeled pixels and labeled regions, we further propose a region-kernel-based support vector machine (RKSVM) classification framework. In RKSVM, three different composite kernels are constructed to describe the joint spatial-spectral similarity. Particularly, we design a desirable stack composite kernel that consists of the point-based kernel, the region-based kernel, and the cross point-to-region kernel. The effectiveness of the proposed RKSVM is validated on three benchmark hyperspectral data sets. Experimental results show the superiority of our region kernel method over the classical point kernel methods. Numéro de notice : A2015-526 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2410991 Date de publication en ligne : 06/04/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2410991 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77554
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 9 (September 2015) . - pp 4810 - 4824[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015091 SL Revue Centre de documentation Revues en salle Disponible A local contrast method for small infrared target detection / C.L. Philip Chen in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 2 (January 2014)
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Titre : A local contrast method for small infrared target detection Type de document : Article/Communication Auteurs : C.L. Philip Chen, Auteur ; Hong Li, Auteur ; Yantao Wei, Auteur ; Tian Xia, Auteur ; Yuan Yan Tang, Auteur Année de publication : 2014 Article en page(s) : pp 574 - 581 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] amélioration du contraste
[Termes IGN] contraste local
[Termes IGN] détection de cible
[Termes IGN] filtrage du bruit
[Termes IGN] image infrarouge
[Termes IGN] rapport signal sur bruit
[Termes IGN] seuillage d'imageRésumé : (Auteur) Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Consequently, an effective small target detection algorithm inspired by the contrast mechanism of human vision system and derived kernel model is presented in this paper. At the first stage, the local contrast map of the input image is obtained using the proposed local contrast measure which measures the dissimilarity between the current location and its neighborhoods. In this way, target signal enhancement and background clutter suppression are achieved simultaneously. At the second stage, an adaptive threshold is adopted to segment the target. The experiments on two sequences have validated the detection capability of the proposed target detection method. Experimental evaluation results show that our method is simple and effective with respect to detection accuracy. In particular, the proposed method can improve the SNR of the image significantly. Numéro de notice : A2014-041 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2242477 En ligne : https://doi.org/10.1109/TGRS.2013.2242477 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32946
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 1 tome 2 (January 2014) . - pp 574 - 581[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2014011B RAB Revue Centre de documentation En réserve L003 Disponible