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Hyperspectral unmixing has attracted considerable attentions in recent years and some promising algorithms have been developed. In this paper, collaborative representation–based unmixing (CRU) for hyperspectral images is proposed. Different from imposing the sparseness constraint on training samples in sparse representation, collaborative representation emphasizes the collaboration of training samples. Furthermore, its closed form solution greatly improves computational efficiency. In the experiments, synthetic and the real hyperspectral data are used to evaluate the effectiveness and efficiency of the proposed collaborative representation-based hyperspectral unmixing algorithm.