A parallel method of atmospheric correction for multispectral high spatial resolution remote sensing images

Shaoshuai Zhao; Chen Ni; Jing Cao; Zhengqiang Li; Xingfeng Chen; Yan Ma; Leiku Yang; Weizhen Hou; Lili Qie; Bangyu Ge; Li Liu; Jin Xing

doi:10.1117/12.2283380

8 March 2018 A parallel method of atmospheric correction for multispectral high spatial resolution remote sensing images

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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Proceedings Volume 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 1061109 (2018) https://doi.org/10.1117/12.2283380
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

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.

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Shaoshuai Zhao, Chen Ni, Jing Cao, Zhengqiang Li, Xingfeng Chen, Yan Ma, Leiku Yang, Weizhen Hou, Lili Qie, Bangyu Ge, Li Liu, and Jin Xing "A parallel method of atmospheric correction for multispectral high spatial resolution remote sensing images", Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 1061109 (8 March 2018); https://doi.org/10.1117/12.2283380

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KEYWORDS

Atmospheric corrections

Remote sensing

Multispectral imaging

Spatial resolution

Image processing

Image segmentation

Satellites

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