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 representa-tion 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.