@inproceedings{058605e301e345cd8e08be87906d09bc,
title = "Research on fast hyperspectral atmospheric radiation transfer imaging modeling based on.NET environment",
abstract = "Atmospheric radiation transmission is one of the most complex and variable parts of hyperspectral remote sensing systems. Aimed at the abstraction and complexity of the influence of atmospheric radiation on the quality of hyperspectral imaging, the design of simulation software for hyperspectral atmospheric radiation transmission imaging in visible light is proposed. Firstly,this paper analyzes the radiation transmission process including the surface reflectivity, the adjacent pixel reflectivity and the atmospheric transmission factor, and describes the calculation method of the radiance at-sensor for the hyperspectral image in the visible light bands. Then the multi-core CPU based on the.Net environment is constructed. The adjacent pixel point diffusion function parallel computing module and the GPU-based on-satellite reflectivity parallel computing module; the experimental part takes the hyperspectral surface reflectance image as input data, and degenerates the output into the hyperspectral radiance simulation data in different scenarios. At the same time, test of the time of individual modules and the overall algorithm in the simulation process is tested. The experimental results of real-time performance show that the parallel algorithm has significantly improved.",
keywords = "C#, atmospheric radiative transmission, hyperspectral at-sensor radiance, image simulation, parallel computing",
author = "Yunqiao Xi and Xiaomei Chen and Tian Lan",
note = "Publisher Copyright: {\textcopyright} 2019 SPIE.; Applied Optics and Photonics China 2019: Optoelectronic Devices and Integration; and Terahertz Technology and Applications, AOPC 2019 ; Conference date: 07-07-2019 Through 09-07-2019",
year = "2019",
doi = "10.1117/12.2547669",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Zhiping Zhou and Xiaocong Yuan and Daoxin Dai",
booktitle = "AOPC 2019",
address = "United States",
}