CPU/GPU Cooperative Fast Band Registration Method for Multispectral Imagery

FANG Liuyang; WANG Mi; PAN Jun

doi:10.13203/j.whugis20160218

Volume 43 Issue 7

Jul. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(7): 1000-1007. > DOI: 10.13203/j.whugis20160218

FANG Liuyang, WANG Mi, PAN Jun. CPU/GPU Cooperative Fast Band Registration Method for Multispectral Imagery[J]. Geomatics and Information Science of Wuhan University, 2018, 43(7): 1000-1007. DOI: 10.13203/j.whugis20160218

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CPU/GPU Cooperative Fast Band Registration Method for Multispectral Imagery

1.
National Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consultants, Kunming 650041, China
2.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Funds:

he National Natural Science Foundation of China 41601476

Research Project of Broadvision Engineering Consultants ZL-2015-03

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Author Bio:
FANG Liuyang, PhD, engineer, specializes in remote sensing data processing and traffic disaster prevention technology.E-mail:fangliuyang@stwip.com
Received Date: March 26, 2017
Published Date: July 04, 2018

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Abstract

Abstract

With the rapid increase of data size of remote sensing images, the traditional serial band re-gistration method cannot meet the demand for real-time processing of big-data multispectral images. Therefore, a CPU/GPU cooperative fast band registration method for multispectral imagery is proposed in this paper. Firstly, the computational amount and degree of parallelism are analyzed; point matching and differential rectification are ported to GPU to execute while the affine transformation parameter is still calculated on CPU. Secondly, kernel task assignment and basic settings are made to ensure the two above GPU steps executable. Moreover, three performance optimization methods, including memory access optimization, instruction optimization and transmission/computation overlap, are designed to further improve the efficiency of band registration. The experimental results based on NVIDIA Tesla M2050 GPU and Intel Xeon E5650 CPU show that the running time of YG-26 multispectral image band registration is only 3.25 s with our method, which got a speedup ratio of 32.32 compared with the traditional CPU serial method. The proposed method can provide quasi-real-time processing capability for multispectral imagery with big data size.
- CPU/GPU cooperation,
- band registration,
- calculation and parallelism analysis,
- kernel task assignment,
- performance optimization

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