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- W2657906002 abstract "Change detection (CD) for multitemporal hyperspectral images (HSI) consists of two steps, change feature extraction and identification. This paper proposes a novel spectrally-spatially regularized low-rank and sparse decomposition model (LRSD_SS), to extract clean change features from corrupted spectral change vectors (SCV) of multitemporal HSI. It decomposes SCV into spatially smoothed low-rank data, sparse outliers and Gaussian noise. The experimental results validate the effectiveness and the efficiency of LRSD_SS." @default.
- W2657906002 created "2017-06-30" @default.
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- W2657906002 date "2017-05-01" @default.
- W2657906002 modified "2023-10-12" @default.
- W2657906002 title "Low-rank matrix decomposition with a spectral-spatial regularization for change detection in hyperspectral imagery" @default.
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- W2657906002 doi "https://doi.org/10.1109/rsip.2017.7958816" @default.
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