关键词: CUDA架构, 高光谱遥感影像, 光谱角填图, 常量存储器, 共享存储器, 存储器合并访问

Abstract: Aiming at the parallel processing of remote sensing image classification, the existing researches are generally carried out through computer cluster and workstation. These ways have the disadvantage of high cost and are difficult to establish. Only a few researches which are based on GPU mainly intend to demonstrate?the availability of this parallel architecture from the perspective of workflow and pay little attention to the significant storage optimization strategies. Directed?against?the shortages of the existing studies, taking the high performance computing of hyperspectral image classification using the method of spectrum waveform matching and spectral angle mapping based on CUDA for example, this paper places emphasis on researching the optimization strategies of GPU storage and their improvement method. The experimental results show that, the optimization strategies of GPU storage and their improvements are effective, besides, for a variety of images of different sizes and data volume, the efficiency of algorithm has been promoted remarkably compared with the situation before these strategies are applied. At the same time, The hyperspectral image classification?based on CUDA acquires accurate computing?results.

Key words: CUDA, hyperspectral remote sensing image, spectral angle mapping, constant memory, shared memory, merged accessing of memory