Superpixel-Based Noise-Robust Sparse Unmixing of Hyperspectral Image

Sparse unmixing (SU) of hyperspectral image (HSI), as a semisupervised approach, aims to find the optimal subset of the spectral library known in advance to represent each pixel in HSI. However, most of the existing SU methods cannot take full advantage of spatial information and mixed noise in HSI. To this end, we propose a superpixel-based noise-robust SU method (SNRSU) in the presence of mixed noise. First, we perform superpixel segmentation (SS) on the first principal component of HSI to extract the homogeneous regions. Then, we unmix each superpixel based on sparse representation (SR) and low-rank representation (LRR) in the maximum a posteriori framework, which can make full use of the spatial-spectral information in HSI under complex mixed noise. A number of experiments on simulated and real HSI datasets confirm the superior performance of the proposed SNRSU both qualitatively and quantitatively.