Robust Double Spatial Regularization Sparse Hyperspectral Unmixing

Abstract

With the help of endmember spectral library, sparse unmixing techniques have been successfully applied to hyperspectral image interpretation. The inclusion of spatial information in the sparse unmixing significantly improves the resulting fractional abundances. However, most existing spatial sparse unmixing algorithms are sensitive to noise and produce unstable solutions. To alleviate this drawback, a new robust double spatial regularization sparse unmixing (RDSRSU) method is proposed, which simultaneously exploits the spatial structure information from hyperspectral images and estimated abundance maps to mitigate the negative influence of noise on unmixing, so as to achieve robust sparse unmixing. To this end, a pre-calculated spatial weighting factor is introduced to maintain the original spatial information of the hyperspectral image. Meanwhile, the total variation spatial regularizer is used to capture the piecewise smooth structure of each abundance map. The experimental results, conducted by two sets of simulated data, as well as Cuprite and Mangrove real hyperspectral data, uncover that the proposed RDSRSU algorithm can offer better anti-noise ability and obtain more accurate results over those gave by other advanced sparse unmixing algorithms.