Weighted Total Variation Regularized Blind Unmixing for Hyperspectral Image

Hyperspectral unmixing plays an important role in hyperspectral imagery (HSI) processing. Numerous unmixing algorithms have been proposed over the last decades. In this letter, we focus on the blind source separation model, which has drawn much attention in the hyperspectral community. However, the nonconvexity of the blind unmixing method often suffers from undesired solution. Thus, additional assumptions and regularizations are required to advance the unmixing performance. In this work, we proposed a new weighted total variation regularized blind unmixing (wtvBU) for HSI. The nonconvex sparsity-inducing function log-exp was exploited to build the weight matrix, which promotes the smooth transitions in the abundance map while preserving the spatial contextual information of the image scene. The proposed algorithm was efficiently solved via the alternating direction method of multipliers. Experimental results on two benchmark HSIs demonstrated that wtvBU achieved competitive performance when compared with other state-of-the-art unmixing algorithms.