Multi-GPU Parallel Implementation of Spatial-Spectral Kernel Sparse Representation for Hyperspectral Image Classification

Classification is one of the major research fields in hyperspectral imagery. Due to the fact that neighboring pixels are more likely to share the same label, it is practical to use spatial information in hyperspectral image to achieve higher accuracy. On the other hand, however, spatial information also leads to higher computational complexity. This paper proposes an efficient implementation of a spatial-spectral kernel sparse representation for hyperspectral image classification base on the multi-GPU platform. The proposed implementation takes advantage of the capability of compute-unified device architecture (CUDA), such as shared memory, streams and peer-to-peer (P2P) transfer of data. In addition, an improvement of performance can be achieved by calculation reorganization and bandwidth usage optimization. Experimental results demonstrate that the proposed method achieves an up to 56.81X speedup in computation time while guaranteeing the classification accuracy.