Hyperspectral unmixing based on iteratively re-weighted smoothed l₀ regularisation
by Yan Li; Chengzhi Deng; Shengqian Wang; Hui Wang
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 11, No. 3, 2016

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, L_1/2 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.

Online publication date: Sat, 24-Dec-2016

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