基于GPU的空谱联合核稀疏表示高光谱分类并行优化

高光谱图像分类是遥感信息处理领域的热点问题，在核稀疏表示分类框架下，联合光谱信息和像元空间信息，空谱联合核稀疏表示高光谱图像分类能够取得较好的分类效果,但较高的计算复杂度及高光谱图像较大的数据量限制了其在实时性要求较高情况下的应用。基于GPU/CUDA架构，提出了一种空谱联合核稀疏表示高光谱分类的并行优化方法，设计访存优化策略对主机和设备端数据交互进行优化；充分利用GPU并行计算能力，加速分类过程中核矩阵的计算；采用依据GPU并行特性实现的矩阵运算，优化基于交替方向乘子法的分类模型求解过程。利用实际高光谱图像数据进行的实验，验证了该方法的有效性和高效性。

GPU based parallel optimization of spatial-spectral kernel sparse representation for hyperspectral image classification

Hyperspectral image classification is a hot issue of hyperspectral remote sensing information processing. Under the structure of kernel sparse representation classification, Spatial Spectral Kernel Sparse Representation Classification (SSKSRC) of hyperspectral images can achieve better performance by joint spectral features and information of spatially adjacent pixels. However, it is impossible to utilize it in time critical condition because of the large scale of data and calculation. A parallel optimization method of SSKSRC is proposed based on GPU/CUDA. A memory access optimization strategy is designed to optimize the data exchange between the host and the device. The parallel computing ability of GPU is fully used to accelerate the calculation of the kernel matrix in the process of classification. The matrix operation that is realized according to the parallel feature of GPU is used to optimize the solving process of the classification model based on the alternating direction multiplier method. The experiments with real hyperspectral image data validate the effectiveness and efficiency of the proposed method.