Total variation and ℓq based hyperspectral unmixing for feature extraction and classification

Blind hyperspectral unmixing jointly estimates both the endmembers and the abundances of hyperspectral images. The endmembers represent the spectral signatures of material found in the image and the abundances specify the amount of each material seen in each pixel in the image. In this paper, a blind hyperspectral unmixing method for feature extraction and classification using total variation (TV) and ℓ_q sparse regularization is proposed. The abundances found are used as features for classification. The classification results are compared to results obtained using Principal Component analysis (PCA) and also to results obtained using hyperspectral unmixing using only TV and sparsity, respectively.