A Novel Collaborative Representation Algorithm for Spectral Unmixing of Hyperspectral Remotely Sensed Imagery

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