Structure-Aware Collaborative Representation for Hyperspectral Image Classification

Recently, collaborative representation (CR) has drawn increasing attention in hyperspectral image classification due to its simplicity and effectiveness. However, existing representation-based classifiers do not explicitly utilize class label information of training samples in estimating representation coefficients. To solve this issue, a structure-aware CR with Tikhonov regularization (SaCRT) method is proposed to consider both class label information of training samples and spectral signatures of testing pixels to estimate more discriminative representation coefficients. In the proposed framework, marginal regression is employed; furthermore, an interclass row-sparsity structure is designed to preserve the compact relationship among intraclass pixels and more separable interclass pixels, thereby enhancing class separability. The experimental results evaluated using three hyperspectral data sets demonstrate that the proposed method significantly outperforms some state-of-the-art classifiers.