GPU Parallel Optimization of Hyperspectral Image Kernel Sparse Representation Classification Based on Spatial-Spectral Graph Regularization

With the development of hyperspectral remote sensing information processing, hyperspectral image classification becomes a hot topic. The algorithm of kernel sparse representation classification based on spatial-spectral graph regularization and sparsity concentration index (SSGSCI-KSRC) gains a good result. Due to the big scale of hyperspectral image data, time-critical requirement in the practical application makes it impossible to use the original SSGSCI-KSRC algorithm. This paper proposes a parallelization method for SSGSCI-KSRC algorithm. The optimization method achieves the efficient calculation operations of hyperspectral image matrix data, coalesces memory accesses to reduce the time of transferring data to the GPU devices, and designs proper kernel functions in the classification algorithm. The experimental results demonstrate that the parallel SSGSCI-KSRC algorithm obtains a better result in terms of computational performance when the accuracy stays the same.