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• W3210330720 abstract "Estimating endmembers and corresponding abundances from mixed pixels are essential steps for hyperspectral unmixing. In hyperspectral unmixing, obtaining accurate unmixing results is difficult since less prior knowledge is available. Besides, the unmixing results are influenced by noise and highly correlated endmembers, so that the obtained abundance maps exist small values which are not present in the image. In this paper, we separate each abundance map into a feature layer and a sparse layer to protect the obtained abundance maps from the above-mentioned factors. The feature layer represents the main information of the abundance map. And the sparse layer contains outliers dominated by the above-mentioned factors. In particular, we design a feature extraction regularization to describe the feature layer and use a weighted ℓ1 norm to describe the sparse layer. Then, under the framework of non-negative tensor factorization (NTF), we propose a novel unmixing algorithm named spatial feature extraction NTF (SFE-NTF) for hyperspectral unmixing. The proposed SFE-NTF is based on an augmented multiplicative algorithm. Experimental results on both synthetic and real hyperspectral data demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms." @default.
• W3210330720 created "2021-11-08" @default.
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• W3210330720 date "2022-03-01" @default.
• W3210330720 modified "2023-10-02" @default.
• W3210330720 title "Spatial feature extraction non-negative tensor factorization for hyperspectral unmixing" @default.
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• W3210330720 doi "https://doi.org/10.1016/j.apm.2021.09.043" @default.
• W3210330720 hasPublicationYear "2022" @default.
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