Change detection in multitemporal hyperspectral images (HSI) can be regarded as a classification task, consisting of two steps: change feature extraction and identification. To extract clean change features from heavily corrupted spectral change vectors (SCV) of multitemporal HSI, this paper proposes a novel spectrally-spatially regularized low-rank and sparse decomposition model (LRSDSS). It exploits the underlying data structure of SCV by decomposing SCV into three components: spatially smoothed low-rank data, sparse outliers and Gaussian noise. The first component maintains clean change features. The second and the third are corruptions to be removed. The experimental results can validate the effectiveness and the efficiency of LRSD_SS.