The methodology of sparse representations (SRs) has being popular in hyperspectral image (HSI) classification. To boost the SR-based classification for HSIs, in this paper we present a designation of sparse representation involving random subspace. First, random band selection or random projection generates data subspaces from an original HSI. Then, the sparse representation on each subspace is solved by a linear programming. Finally, the average of the sparse representations or that of the associated local residuals is applied to integrate the contribution of all subspaces, yielding the final classification output. Four SR classifiers, including RSBS-AC, RSBS-AR, RSP-AC and RSP-AR, are therefore established. Experimental results from a real HSI suggest that such SR classifiers truly achieve significant enhanced classification performance.
['Lin He', 'Yizhou Rao']
Random subspace based sparse representation for hyperspectral image classification