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- W4386175655 abstract "In semi-supervised unmixing, mixed pixels in a hyperspectral image can be decomposed into corresponding abundances based on the known endmember library. The hyperspectral image contains important spatial information about the land cover, which can help enhance the performance of hyperspectral unmixing. In this letter, we proposed the first-order graph trend filtering for sparse unmixing to explore and utilize the spatial information more effectively and accurately. The proposed method adaptively constructs the first-order graph difference operator from the original data, and then uses double reweighted ℓ <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> -norm regularization to promote the sparsity of the abundances. The results of experiments on simulated and real datasets show that the proposed algorithm can more accurately utilize the spatial structure and outperform competing methods." @default.
- W4386175655 created "2023-08-26" @default.
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- W4386175655 date "2023-01-01" @default.
- W4386175655 modified "2023-10-14" @default.
- W4386175655 title "First-order graph trend filtering for sparse hyperspectral unmixing" @default.
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- W4386175655 doi "https://doi.org/10.1109/lgrs.2023.3307891" @default.
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