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• W2952398309 abstract "Incorporating spatial information into hyperspectral unmixing procedures has been shown to have positive effects, due to the inherent spatial-spectral duality in hyperspectral scenes. Current research works that consider spatial information are mainly focused on the linear mixing model. In this paper, we investigate a variational approach to incorporating spatial correlation into a nonlinear unmixing procedure. A nonlinear algorithm operating in reproducing kernel Hilbert spaces, associated with an $ell_1$ local variation norm as the spatial regularizer, is derived. Experimental results, with both synthetic and real data, illustrate the effectiveness of the proposed scheme." @default.
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• W2952398309 date "2013-10-31" @default.
• W2952398309 modified "2023-09-25" @default.
• W2952398309 title "Nonlinear unmixing of hyperspectral images using a semiparametric model and spatial regularization" @default.
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• W2952398309 doi "https://doi.org/10.48550/arxiv.1310.8612" @default.
• W2952398309 hasPublicationYear "2013" @default.
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