Multi-GPU Implementation of Nearest-Regularized Subspace Classifier for Hyperspectral Image Classification

The classification of hyperspectral imagery (HSI) is an important part of HSI applications. The nearest-regularized subspace (NRS) is an effective method to classify HSI as one of the sparse representation methods. However, its high computational complexity confines usage in a time-critical scene. In order to enhance the computation efficiency of the NRS classifier, this article proposed a new parallel implementation on the graphics processing unit (GPU). First of all, an optimized single-GPU algorithm is designed for parallel computing, and then the multi-GPU version is developed to improve the efficiency of parallel computing. In addition, optimal parameters for the data stream and memory strategy are put forward to adapt a parallel environment. In order to verify the algorithm's effectiveness, the serial algorithm based on central processing unit is used for a comparative experiment. The performance of the multi-GPU approach is tested by two hyperspectral image datasets. Compared with the serial algorithm, the multi-GPU method with four GPUs can achieve up to 360× acceleration.