Superpixel-Based Weighted Sparse Regression and Spectral Similarity Constrained for Hyperspectral Unmixing

With the support of spectral libraries, sparse unmixing techniques have gradually developed. However, some existing sparse unmixing algorithms suffer from problems, such as insufficient utilization of spatial information and sensitivity to noise. To solve these problems, this article proposes a novel hyperspectral unmixing algorithm, called superpixel-based weighted sparse regression and spectral similarity constrained unmixing. In the proposed method, a precalculated weight is introduced to help enhance sparsity of abundances, which is obtained from coarse abundance estimation. It also maintains spatial consistency in a local region of a hyperspectral image to mitigate the negative influence of noise. Additionally, the method selects optimal neighborhood pixels in the local region by combining spatial and spectral information and constructs a similarity matrix to explore spectral similarity in the subspace. Meanwhile, superpixel segmentation is considered as an auxiliary method to obtain local regions in the unmixing process. Experiments performed on synthetic and real data demonstrate that the proposed method achieves more accurate abundance estimation than other comparison algorithms.