  Sparse regression methods have been proven effective in a wide range of
signal processing problems such as image compression, speech coding, channel
equalization, linear regression and classification. In this paper a new convex
method of hyperspectral image classification is developed based on the sparse
unmixing algorithm SUnSAL for which a pixel adaptive L1-norm regularization
term is introduced. To further enhance class separability, the algorithm is
kernelized using an RBF kernel and the final results are improved by a
combination of spatial pre and post-processing operations. It is shown that the
proposed method is competitive with state of the art algorithms such as SVM-CK,
KSOMP-CK and KSSP-CK.
