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,
l
₁
norm minimization is used to approximate the
l
₀
norm minimization problem in terms of computational complexity. In this letter, we model the hyperspectral unmixing as a constrained sparse
lp
-
l
₂
(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
l
₁
-
l
₂
method.