3D total variation hyperspectral compressive sensing using unmixing
To reduce the huge resource consumption in the hyperspectral imaging and transmission, this paper proposes a high-performance compression method. Specially, a novel 3D total variation prior is imposed on abundance fractions of end-members. In this method, compressed data is obtained by a random observation matrix in a compressive sensing way. Based on the hyperspectral linear mixed model and known endmembers, abundance fractions are estimated by an augmented Lagrangian method with the devised prior and then the original data is reconstructed. Extensive experimental results demonstrate the superiority of the proposed method to several state-of-art methods.