Sparse Representation-Based Nearest Neighbor Classifiers for Hyperspectral Imagery

Abstract

In this letter, a sparse representation-based nearest neighbor (SRNN) classifier is proposed. Unlike the traditional $k$ -nearest neighbor (NN) classifier that employs the Euclidean distance as similarity metric, the proposed SRNN considers sparse coefficients to determine the label of testing samples, since sparse coefficients can reflect the similarity between data and provide more discriminative information. A local SRNN (LSRNN) classifier is also proposed to utilize class-specific sparse coefficients to improve the performance. Furthermore, due to the fact that neighboring pixels tend to belong to the same class with high probability, a spatially joint version of LSRNN, called JSRNN, is developed to further improve LSRNN. The proposed SRNN, LSRNN, and JSRNN have been validated on several hyperspectral remote sensing image data sets. Experimental results demonstrate that the proposed classifiers increase the classification accuracy compared with the traditional $k$ -NN, local mean-based NN (LMNN) classifiers, and original sparse representation classifiers using representation residuals.