Low-rank matrix decomposition with a spectral-spatial regularization for change detection in hyperspectral imagery

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.