Tangent Distance-Based Collaborative Representation for Hyperspectral Image Classification

摘要

Recently, collaborative representation for hyperspectral image analysis has received great interest. Due to the effectiveness of local manifold in a tangent space, this letter extends the collaborative representation classification (CRC) mechanism into the tangent space. Specifically, this letter uses simplified tangent distance and a new regularization term and designs a modified classifier innovatively. Moreover, two variants with weighted diagonal matrices to adaptively adjust the regularization terms are developed to further improve the classification performance. In the experiments, two real hyperspectral images were adopted for performance evaluation, and the experimental results demonstrate that the proposed algorithms can significantly improve classification results compared with the original CRC algorithm and other related classifiers.