Locality Sensitive Discriminant Analysis for Group Sparse Representation-Based Hyperspectral Imagery Classification

Haoyang Yu, Lianru Gao, Wei Li, Qian Du, Bing Zhang

Abstract: This letter proposes to integrate the locality sensitive discriminant analysis (LSDA) with the group sparse representation (GSR) for a hyperspectral imagery classification. The LSDA is to project the data set to a lower-dimensional subspace to preserve local manifold structure and discriminant information, while the GSR is to encode the projected testing set as a sparse linear combination of group-structured training samples for classification. The proposed approach, denoted as LSDA-GSR classifier (GSRC), is evaluated using two real hyperspectral data sets. Experimental results demonstrate that it can provide considerable improvement to the original counterparts, i.e., SRC and GSRC, with a relatively low computational cost.