Sparse Hyperspectral Unmixing Based on Constrained lp - l2 Optimization

Linear spectral unmixing is an effective technique to estimate the abundances of materials present in each hyperspectral image pixel. Recently, sparse-regression-based unmixing approaches have been proposed to tackle this problem. Mostly, l₁ norm minimization is used to approximate the l₀ norm minimization problem in terms of computational complexity. In this letter, we model the hyperspectral unmixing as a constrained sparse lp - l₂(0 <; p <; 1) optimization problem and propose to solve it via the iteratively reweighted least squares algorithm. Experimental results on a series of simulated data sets and a real hyperspectral image demonstrate that the proposed method can achieve performance improvement over the state-of-the-art l₁ - l₂ method.