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Proceedings Paper

A parallel method of atmospheric correction for multispectral high spatial resolution remote sensing images
Author(s): Shaoshuai Zhao; Chen Ni; Jing Cao; Zhengqiang Li; Xingfeng Chen; Yan Ma; Leiku Yang; Weizhen Hou; Lili Qie; Bangyu Ge; Li Liu; Jin Xing
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Paper Abstract

The remote sensing image is usually polluted by atmosphere components especially like aerosol particles. For the quantitative remote sensing applications, the radiative transfer model based atmospheric correction is used to get the reflectance with decoupling the atmosphere and surface by consuming a long computational time. The parallel computing is a solution method for the temporal acceleration. The parallel strategy which uses multi-CPU to work simultaneously is designed to do atmospheric correction for a multispectral remote sensing image. The parallel framework’s flow and the main parallel body of atmospheric correction are described. Then, the multispectral remote sensing image of the Chinese Gaofen-2 satellite is used to test the acceleration efficiency. When the CPU number is increasing from 1 to 8, the computational speed is also increasing. The biggest acceleration rate is 6.5. Under the 8 CPU working mode, the whole image atmospheric correction costs 4 minutes.

Paper Details

Date Published: 8 March 2018
PDF: 5 pages
Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 1061109 (8 March 2018); doi: 10.1117/12.2283380
Show Author Affiliations
Shaoshuai Zhao, Henan Polytechnic Univ. (China)
Institute of Remote Sensing and Digital Earth (China)
Chen Ni, China Academy of Space Technology (China)
Jing Cao, China Academy of Space Technology (China)
Zhengqiang Li, Institute of Remote Sensing and Digital Earth (China)
Xingfeng Chen, Institute of Remote Sensing and Digital Earth (China)
Yan Ma, Institute of Remote Sensing and Digital Earth (China)
Leiku Yang, Henan Polytechnic Univ. (China)
Weizhen Hou, Institute of Remote Sensing and Digital Earth (China)
Lili Qie, Institute of Remote Sensing and Digital Earth (China)
Bangyu Ge, Institute of Remote Sensing and Digital Earth (China)
Li Liu, China Ctr. for Resources Satellite Data and Application (China)
Jin Xing, China National Space Administration (China)


Published in SPIE Proceedings Vol. 10611:
MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Nong Sang; Jie Ma; Zhong Chen, Editor(s)

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