Spatial-Aware Collaborative Representation for Hyperspectral Remote Sensing Image Classification

Representation-residual-based classifiers have attracted much attention in recent years in hyperspectral image (HSI) classification. How to obtain the optimal representation coefficients for the classification task is the key problem of these methods. In this letter, spatial-aware collaborative representation (CR) is proposed for HSI classification. In order to make full use of the spatial-spectral information, we propose a closed-form solution, in which the spatial and spectral features are both utilized to induce the distance-weighted regularization terms. Different from traditional CR-based HSI classification algorithms, which model the spatial feature in a preprocessing or postprocessing stage, we directly incorporate the spatial information by adding a spatial regularization term to the representation objective function. The experimental results on three HSI data sets verify that our proposed approach outperforms the state-of-the-art classifiers.