Blind Hyperspectral Unmixing with Enhanced 2DTV Regularization Term

Abstract

For the problem where the existing hyperspectral unmixing methods do not take full advantage of the correlations and differences between all these bands, resulting in affecting the final unmixing results, we design an enhanced 2DTV (E-2DTV) regularization term and suggest a blind hyperspectral unmixing method with the E-2DTV regularization term (E-gTVMBO), which adds E-2DTV regularization to the previous blind hyperspectral unmixing based on g-TV model. The E-2DTV regularization term is based on the gradient mapping of all bands of HSI, and the sparsity is calculated on the basis of the subspace, rather than applying sparsity to the gradient map itself, which can utilize the correlations and differences between all bands naturally. The experimental results prove the superiority of the E-gTVMBO method from both qualitative and quantitative perspectives. The research results can be applied to land cover classification, mineral analysis, and other fields.