Joint Sparsity and Total Variation Based Unmixing of Mixed Noise

Abstract

Abstract Hyperspectral unmixing is the procedure by which the end component elements are calculated and their fractional abundances are found in each pixel in hyperspectral images. Sometimes several types of sound harm a hyperspectral photo. In a general scenario that takes mixed noise into consideration, this study addresses the hyperspectral non-mixing problem. Gaussian and sparse noises are expressly taken into account in the unmixing model. The problem of unmixing was formulated to use the combined shortage of abundance maps. For the modelling of flatness diagrams, a complete variation-based regularisation has also been used. For the solution of an algorithm for the optimization problem, the split-Bregman technique was used. The advantages of the method proposed are revealed by detailed preliminary findings on both real and synthetic images.