An analysis of collaborative representation schemes for the classification of hyperspectral images

Collaborative-based representation classifiers have widely spread in the latest years achieving remarkable results in signal and image processing tasks. In this paper, we consider these approaches for the hyperspectral image classification. Specifically, we focus on collaborative and sparse representation classifiers and we perform an investigation on the role of the different regularizations and constraints that can be considered with respect to the classification performance. In addition, we propose to consider the Nearest Subspace Classifier with regularization which, from the experiments, has proven to be a competitive classification technique. Experimental results have been conducted considering both spectral and spatial features of a real hyperspectral image.