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Proceedings Paper

The fast processing method for the recognition of atmospheric profiles' characters based on artificial neural network
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Paper Abstract

The goal of this paper is to introduce how to make use of the artificial neural network technique to develop a new method which can fast recognize atmospheric profiles' characters from hyperspectral infrared thermal remote sensing. This technique would accelerate the calculation speed of hyperspectral infrared atmospheric radiative transfer model (RTM). As the launch of hyperspectral infrared sensors such as Infrared Atmospheric Sounding Interferometer (IASI), it becomes possible for people to take advantage of the hyperspectral data which contains abundance of precise spectral information, to add constraint conditions for the researches of some physical models. But in practice, normal hyperspectral infrared atmospheric RTM are relatively complex and time costing. The calculation speed of these models is not fast enough to make these models to respond to the variety of atmospheric radiative, or the bright temperature timely. Therefore, the practical and effective physical models and research methods, such as the practical surface temperate inversion model, couldn't be founded relay on these transfer models. In order to solve this problem, institutions and researchers around the world have tried some methods to develop the fast calculation of atmospheric RTM. But these methods still have problems on speed, accuracy and the applicability for certain sensors.

Paper Details

Date Published: 30 October 2009
PDF: 9 pages
Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 749806 (30 October 2009); doi: 10.1117/12.829597
Show Author Affiliations
Min-Jie Wu, Academy of Opto-Electronics (China)
Graduate School of Chinese Academy of Sciences (China)
Xiao-Guang Jiang, Academy of Opto-Electronics (China)
Bo-Hui Tang, Institute of Geographical Sciences and Natural Resources Research (China)
Zhao-Liang Li, Institute of Geographical Sciences and Natural Resources Research (China)
Univ. Louis Pasteur-Strasbourg I (France)


Published in SPIE Proceedings Vol. 7498:
MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications
Faxiong Zhang; Faxiong Zhang, Editor(s)

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