Spectral-spatial joint sparsity unmixing of hyperspectral images based on framelet transform

The purpose of hyperspectral unmixing is to estimate the spectral signatures composing the data (endmembers) and their abundance fractions. However, the conventional sparse unmixing involves finding the optimal subset of signatures for the observed data in a very large standard spectral library in the spatial domain and the spatial domain information has many drawbacks which are very scattered, redundancy, and susceptible to noise. In this paper, a new sparse unmixing algorithm is based on framelet domain, namely spectral-spatial joint sparsity unmixing of hyperspectral images based on framelet transform (SSFSU), is proposed to complete the unmixing task of hyperspectral remote sensing images. SSFSU can improve the efficiency of data feature extraction and enhance the anti-noise performance by using the framelet transform information. the experimental results of synthetic and real data show that the SSFSU algorithm has better anti-noise performance and unmixing results compared with other advanced sparse unmixing methods.