Supervised Dimensionality Reduction of Hyperspectral Imagery via Local and Global Sparse Representation

Despite the successful applications of unsupervised sparse dimensionality reduction (USDR) in pattern recognition, the USDR still suffers from two challenges for hyperspectral images (HSIs), which limit its discriminative performance: first, it cannot be applied for dimensionality reduction using both training samples and testing samples; second, it lacks the ability to integrate the spectral with spatial information for improving the discriminative performance of HSIs. In order to tackle the first challenge, we extend it to a supervised scenario, which can be applied for both training samples and testing samples, namely dimensionality reduction sparse representation (DRSR). Then, we propose a novel method called local and global DRSR (LGDRSR) to integrate the spectral information and spatial information of HSIs to further improve the discriminative performance of HSIs. The proposed LGDRSR computes the distance information between pixels of HSIs including the whole samples and their corresponding locations with a unified metric matrix. Experimental results show the proposed LGDRSR outperforms other state-of-the-art algorithms significantly.