VALIDATION AND ANALYSIS OF HIGH PERFORMANCE COMPUTATION ON HYPERSPECTRAL IMAGERY BASED ON GPU

XU Ning; XIAO Xin-yao; HU Yu-xin; WEN Jing; WANG Da-ming

2015 Vol. 21, No. 2

Article Contents

Article navigation > Journal of Geomechanics > 2015 > 21(2): 190-198

Next Article Previous Article

XU Ning, XIAO Xin-yao, HU Yu-xin, WEN Jing, 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.

Citation:

XU Ning, XIAO Xin-yao, HU Yu-xin, WEN Jing, 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.

VALIDATION AND ANALYSIS OF HIGH PERFORMANCE COMPUTATION ON HYPERSPECTRAL IMAGERY BASED ON GPU

1.
Key Laboratory of Technology in Geo-spatial Information Processing and Application System, IECAS, Beijing 100190, China
2.
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China
4.
China Aero Geophysical Survey and Remote Sensing Centre for Land and Resources, Beijing 100083, China
5.
Oil & Gas Survey, China Geological Survey, Beijing 100029, China

Received Date: 13 November 2014

Publish Date: 25 June 2015

Abstract

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.
- hyperspectral data /
- GPU /
- high performance computation /
- SAM /
- PPI

FullText(HTML)

References

[1]	王润生, 甘甫平, 闫柏琨, 等.高光谱矿物填图技术与应用研究[J].国土资源遥感, 2010, (1):1~13. doi: 10.6046/gtzyyg.2010.01.01 CrossRef Google Scholar WANG Run-sheng, GAN Fu-ping, YAN Bai-kun, et al. Hyperspectral mineral mapping and its application[J]. Remote Sensing for Land & Resources, 2010, (1): 1~13. doi: 10.6046/gtzyyg.2010.01.01 CrossRef Google Scholar
[2]	王润生, 熊盛青, 聂洪峰, 等.遥感地质勘查技术与应用研究[J].地质学报, 2011, 85(11):1699~1743. Google Scholar WANG Run-sheng, XIONG Sheng-qing, NIE Hong-feng, et al. Remote sensing technology and its application in geological exploration[J]. Acta Geological Sinica, 2011, 85(11): 1699~1743. Google Scholar
[3]	程宾洋. 高光谱遥感蚀变矿物填图算法并行设计与实现[D]. 成都: 成都理工大学, 2013.http://cdmd.cnki.com.cn/Article/CDMD-10616-1013263591.htm Google Scholar CHENG Bin-yang. The parallel design and implementation of hyperspectral remote sensing mineral mapping algorithm[D]. Chengdu: Chengdu University of Technology, 2013. Google Scholar
[4]	Gillis D, Bowles J H. Parallel implementation of the ORASIS algorithm for remote sensing data analysis[C]//Plaza A J, Chang C I. High performance computing in remote sensing. US: Taylor & Francis Group, 2008. Google Scholar
[5]	Tilton J C. Parallel implementation of the recursive approximation of an unsupervised hierarchical segmentation algorithm[C]// Plaza A J, Chang C I. High performance computing in remote sensing. US: Taylor & Francis Group, 2008. Google Scholar
[6]	Wang Jianwei, Chang Chein-I. FPGA design for real-time implementation of constrained energy minimization for hyperspectral target detection[C]// Plaza A J, Chang C I. High performance computing in remote sensing. US: Taylor & Francis Group, 2008. Google Scholar
[7]	Setoain J, Prieto M, Tenllado C, et al. Parallel morphological endmember extraction using commodity graphics hardware[J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(3): 441~445. doi: 10.1109/LGRS.2007.897398 CrossRef Google Scholar
[8]	Agathos A, Li J, Petcu D, et al. Multi-GPU implementation of the minimum volume simplex analysis algorithm for hyperspectral unmixing[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2281~2296. doi: 10.1109/JSTARS.2014.2320896 CrossRef Google Scholar
[9]	杨靖宇, 张永生, 董广军.基于GPU的遥感影像SAM分类算法并行化研究[J].测绘科学, 2010, 35(3):9~11. Google Scholar YANG Jing-yu, ZHANG Yong-sheng, DONG Guang-jun. Investigation of parallel method of RS image SAM algorithmic based on GPU[J]. Science of Surveying and Mapping, 2010, 35(3): 9~11. Google Scholar
[10]	罗耀华, 郭科, 赵仕波.基于GPU的高光谱遥感MNF并行方法研究[J].四川师范大学学报:自然科学版, 2013, 36(3):476~479. Google Scholar LUO Yao-hua, GUO Ke, ZHAO Shi-bo. Minimum noise fraction of hyperspectral remote sensing in parallel computing based on GPU[J]. Journal of Sichuan Normal University: Natural Science, 2013, 36(3): 476~479. Google Scholar
[11]	宋义刚, 叶舜, 吴泽彬, 等.基于GPU的高光谱遥感图像PPI并行优化[J].航天返回与遥感, 2014, 35(4):74~80. Google Scholar SONG Yi-gang, YE Shun, WU Ze-bin, et al. Parallel optimization of Pixel Purity Index algorithm based on GPU for hyperspectral remote sensing image[J]. Spacecraft Recovery & Remote Sensing, 2014, 35(4): 74~80. Google Scholar
[12]	Kruse F A, Lefkoff A B, Boardman J W, et al. The Spectral Image Processing System (SIPS): Interactive visualization and analysis of imaging spectrometer data[J]. Remote Sensing of Environment, 1993, 44: 145~163. doi: 10.1016/0034-4257(93)90013-N CrossRef Google Scholar
[13]	Boardman J W. Geometric mixture analysis of imaging spectrometery data[J]. Proc. Int. Geoscience and Remote Sensing Symp, 1994, 4: 2369~2371. Google Scholar
[14]	许宁, 胡玉新, 雷斌, 等.一种基于PPI的高光谱数据矿物信息自动提取方法[J].测绘科学, 2013, 38(4):138~141. Google Scholar XU Ning, HU Yu-xin, LEI Bin, et al. Automated mineral information extraction based on PPI algorithm for hyperspectral image[J]. Science of Surveying and Mapping, 2013, 38(4): 138~141. Google Scholar
[15]	Green A A, Berman M, Switzer P, et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal [J]. IEEE Transactions on Geoscience and Remote Sensing, 1988, 26(1): 65~74. doi: 10.1109/36.3001 CrossRef Google Scholar