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An adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery / Yanfei Zhong in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1411 - 1426 Titre : An adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery Type de document : Article/Communication Auteurs : Yanfei Zhong, Auteur ; Yunyun Wu, Auteur ; Xiong Xu, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1411 - 1426 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse infrapixellaire [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] classification du maximum a posteriori [Termes IGN] classification pixellaire [Termes IGN] image hyperspectrale [Termes IGN] traitement automatique d'images [Termes IGN] traitement de données localisées Résumé : (Auteur) The subpixel mapping technique can specify the spatial distribution of different categories at the subpixel scale by converting the abundance map into a higher resolution image, based on the assumption of spatial dependence. Traditional subpixel mapping algorithms only utilize the low-resolution image obtained by the classification image downsampling and do not consider the spectral unmixing error, which is difficult to account for in real applications. In this paper, to improve the accuracy of the subpixel mapping, an adaptive subpixel mapping method based on a maximum a posteriori (MAP) model and a winner-take-all class determination strategy, namely, AMCDSM, is proposed for hyperspectral remote sensing imagery. In AMCDSM, to better simulate a real remote sensing scene, the low-resolution abundance images are obtained by the spectral unmixing method from the downsampled original image or real low-resolution images. The MAP model is extended by considering the spatial prior models (Laplacian, total variation (TV), and bilateral TV) to obtain the high-resolution subpixel distribution map. To avoid the setting of the regularization parameter, an adaptive parameter selection method is designed to acquire the optimal subpixel mapping results. In addition, in AMCDSM, to take into account the spectral unmixing error in real applications, a winner-take-all strategy is proposed to achieve a better subpixel mapping result. The proposed method was tested on simulated, synthetic, and real hyperspectral images, and the experimental results demonstrate that the AMCDSM algorithm outperforms the traditional subpixel mapping methods and provides a simple and efficient algorithm to regularize the ill-posed subpixel mapping problem. Numéro de notice : A2015-132 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2340734 Date de publication en ligne : 07/08/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2340734 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75796 [article]

Exemplaires(1)

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065-2015031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Adaptive MAP sub-pixel mapping model based on regularization curve for multiple shifted hyperspectral imagery / Yanfei Zhong in ISPRS Journal of photogrammetry and remote sensing, vol 96 (October 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 96 (October 2014) . - pp 134 - 148 Titre : Adaptive MAP sub-pixel mapping model based on regularization curve for multiple shifted hyperspectral imagery Type de document : Article/Communication Auteurs : Yanfei Zhong, Auteur ; Yunyun Wu, Auteur ; Liangpei Zhang, Auteur ; Xiong Xu, Auteur Année de publication : 2014 Article en page(s) : pp 134 - 148 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] décomposition du pixel [Termes IGN] image hyperspectrale Résumé : (Auteur) Sub-pixel mapping is a promising technique for producing a spatial distribution map of different categories at the sub-pixel scale by using the fractional abundance image as the input. The traditional sub-pixel mapping algorithms based on single images often have uncertainty due to insufficient contraint of the sub-pixel land-cover patterns within the low-resolution pixels. To improve the sub-pixel mapping accuracy, sub-pixel mapping algorithms based on auxiliary datasets, e.g., multiple shifted images, have been designed, and the maximum a posteriori (MAP) model has been successfully applied to solve the ill-posed sub-pixel mapping problem. However, the regularization parameter is difficult to set properly. In this paper, to avoid a manually defined regularization parameter, and to utilize the complementary information, a novel adaptive MAP sub-pixel mapping model based on regularization curve, namely AMMSSM, is proposed for hyperspectral remote sensing imagery. In AMMSSM, a regularization curve which includes an L-curve or U-curve method is utilized to adaptively select the regularization parameter. In addition, to take the influence of the sub-pixel spatial information into account, three class determination strategies based on a spatial attraction model, a class determination strategy, and a winner-takes-all method are utilized to obtain the final sub-pixel mapping result. The proposed method was applied to three synthetic images and one real hyperspectral image. The experimental results confirm that the AMMSSM algorithm is an effective option for sub-pixel mapping, compared with the traditional sub-pixel mapping method based on a single image and the latest sub-pixel mapping methods based on multiple shifted images. Numéro de notice : A2014-376 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.06.019 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.06.019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73815 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2014101	RAB	Revue	Centre de documentation	En réserve L003	Disponible