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Spare representation of signals has received significant attention in recentyears. Based on these developments, a sparse representation-basedclassification (SRC) has been proposed for a variety of classification andrelated tasks, including face recognition. Recently, a class dependent variantof SRC was proposed to overcome the limitations of SRC for remote sensing imageclassification. Traditionally, greedy pursuit based method such as orthogonalmatching pursuit (OMP) are used for sparse coefficient recovery due to theirsimplicity as well as low time-complexity. However, orthogonal least square(OLS) has not yet been widely used in classifiers that exploit the sparserepresentation properties of data. Since OLS produces lower signalreconstruction error than OMP under similar conditions, we hypothesize thatmore accurate signal estimation will further improve the classificationperformance of classifiers that exploiting the sparsity of data. In this paper,we present a classification method based on OLS, which implements OLS in aclasswise manner to perform the classification. We also develop and present itskernelized variant to handle nonlinearly separable data. Based on tworeal-world benchmarking hyperspectral datasets, we demonstrate that classdependent OLS based methods outperform several baseline methods includingtraditional SRC and the support vector machine classifier.

Sparse Representation-Based Classification: Orthogonal Least Squares or Orthogonal Matching Pursuit?