Hyperspectral unmixing based on iteratively re-weighted smoothed l<SUB align="right">0 regularisation

Abstract

Hyperspectral unmixing (HU) plays a fundamental role in a wide range of hyperspectral applications. Sparse-based approach has recently received much attention in hyperspectral unmixing area. Sparse unmixing is based on the assumption that each mixed pixel in the hyperspectral image can be expressed in the form of linear combination of a number of pure spectral signatures that are known in advance. Despite the success of sparse unmixing based on the L0, L1/2 or L0 regulariser, the limitation of this approach on its computational complexity or sparsity affects the efficiency or accuracy. In this paper, as iteratively re-weighted algorithm can help to find the much sparser solution in optimal problems, we proposed a sparse unmixing method based on iteratively re-weighted smoothed L0 regularisation, named RSL0SU. Then a variable splitting and augmented Lagrangian algorithm is introduced to solve the optimisation problem. Comparing to SUnSAL method and SL0SU method, our experimental results with both simulated and real hyperspectral data sets demonstrate that the RSL0SU method is an effective and accurate spectral unmixing algorithm for hyperspectral remote sensing imagery.