| GPU用于高光谱数据高性能计算的应用实践与分析 |
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| 引用本文： | 许宁,肖新耀,胡玉新,温静,汪大明.GPU用于高光谱数据高性能计算的应用实践与分析[J].地质力学学报,2015,21(2):190-198. |
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| 作者姓名： | 许宁 肖新耀 胡玉新 温静 汪大明 |
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| 作者单位： | 中国科学院空间信息处理与应用系统技术重点实验室;中国科学院电子学研究所;中国科学院大学;中国国土资源航空物探遥感中心;中国地质调查局油气资源调查中心 |
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| 基金项目： | 中国地质调查局地质调查项目“地质勘查遥感系统集成与综合应用示范”(1212011120226) |
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| 摘 要： | 高光谱遥感数据具有波段多、数据量大、处理复杂等特点,基于GPU的高性能计算在遥感领域得到了快速发展,为高光谱数据的快速处理提供了硬件和技术条件。采用GPU对高光谱遥感数据常用的SAM、PPI等处理算法进行应用实验,验证基于GPU的高光谱遥感数据快速处理技术。实验采用新疆东天山地区的一景星载Hyperion数据,利用支持IDL开发语言的GPULib、CUDA运行时API库进行算法效率的验证,结果表明,基于GPU的高光谱数据处理效率比常规的多核CPU主机处理效率有较大提升,具有一定的应用推广价值。
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| 关 键 词： | 高光谱数据 GPU 高性能计算 SAM PPI |
| 收稿时间： | 2014/11/13 0:00:00 |
VALIDATION AND ANALYSIS OF HIGH PERFORMANCE COMPUTATION ON HYPERSPECTRAL IMAGERY BASED ON GPU |
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| XU Ning,XIAO Xin-yao,HU Yu-xin,WEN Jing and WANG Da-ming.VALIDATION AND ANALYSIS OF HIGH PERFORMANCE COMPUTATION ON HYPERSPECTRAL IMAGERY BASED ON GPU[J].Journal of Geomechanics,2015,21(2):190-198. |
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| Authors: | XU Ning XIAO Xin-yao HU Yu-xin WEN Jing and WANG Da-ming |
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| Institution: | XU Ning;XIAO Xin-yao;HU Yu-xin;WEN Jing;WANG Da-ming;Key Laboratory of Technology in Geo-spatial Information Processing and Application System,IECAS;Institute of Electronics,Chinese Academy of Sciences;University of Chinese Academy of Sciences;China Aero Geophysical Survey and Remote Sensing Centre for Land and Resources;Oil & Gas Survey,China Geological Survey; |
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| Abstract: | Hyperspectral imagery has many characteristics, such as plenty of bands, large volume of data, high computing complexity. In recent years, high performance computation has been making great progress in remote sensing based on GPU, providing the hardware and technical conditions for the rapid processing of hyperspectral data. We implemented the experiments on a hyperspectral image which was obtained by Hyperion of EO-1 satellite in East Tianshan area, Xinjiang, using SAM and PPI algorithms based on CPU and GPU, trying to study the fast processing technology on hyperspectral data.. Actually the GPULib and CUDA API were used through IDL language and the data was tested by different algorithms. The results show that the processing efficiency of hyperspectral data in GPU is greater than CPU and the technology can be used in remote sensing image processing. |
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| Keywords: | hyperspectral data GPU high performance computation SAM PPI |
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