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- W2547263678 abstract "Hyperspectral images play an important role in real-world applications, such as recognition and remote sensing, etc. How to enhance the spatial resolution of hyperspectral image is still a challenging problem in this field. In this paper, we propose a novel hyperspectral image super-resolution approach by jointly incorporating the sparse, low-rank constraints and spectral mixture priori into a linear unmixing framework, which will make the unmixing framework more consistent with the real-world scenarios of the spectral mixture. Experiments on two public databases show that our proposed approach achieves much lower average reconstruction errors than other state-of-the-art methods." @default.
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- W2547263678 date "2016-07-01" @default.
- W2547263678 modified "2023-09-26" @default.
- W2547263678 title "Hyperspectral image super-resolution using sparse spectral unmixing and low-rank constraints" @default.
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- W2547263678 doi "https://doi.org/10.1109/igarss.2016.7730884" @default.
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