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Neural network simulation of the atmospheric point spread function for the adjacency effect research
Ma, Xiaoshan; Wang, Haidong; Li, Ligang; Yang, Zhen; Meng, Xin; Ma, XS (reprint author), Chinese Acad Sci, Natl Space Sci Ctr, Key Lab Elect & Informat Technol Space Syst, Beijing 100190, Peoples R China.
Department空间技术部
Source PublicationOPTICS IN ATMOSPHERIC PROPAGATION AND ADAPTIVE SYSTEMS XIX
2016
Language英语
ISSN0277-786X
ISBN978-1-5106-0408-7; 978-1-5106-0409-4
AbstractAdjacency effect could be regarded as the convolution of the atmospheric point spread function (PSF) and the surface-leaving radiance. Monte Carlo is a common method to simulate the atmospheric PSF. But it can't obtain analytic expression and the meaningful results can be only acquired by statistical analysis of millions of data. A backward Monte Carlo algorithm was employed to simulate photon emitting and propagating in the atmosphere under different conditions. The PSF was determined by recording the photon-receiving numbers in fixed bin at different position. A multilayer feed-forward neural network with a single hidden layer was designed to learn the relationship between the PSF's and the input condition parameters. The neural network used the back-propagation learning rule for training. Its input parameters involved atmosphere condition, spectrum range, observing geometry. The outputs of the network were photon-receiving numbers in the corresponding bin. Because the output units were too many to be allowed by neural network, the large network was divided into a collection of smaller ones. These small networks could be ran simultaneously on many workstations and/or PCs to speed up the training. It is important to note that the simulated PSF's by Monte Carlo technique in non-nadir viewing angles are more complicated than that in nadir conditions which brings difficulties in the design of the neural network. The results obtained show that the neural network approach could be very useful to compute the atmospheric PSF based on the simulated data generated by Monte Carlo method.
KeywordAdjacency Effect Atmospheric Point Spread Function Monte Carlo Simulation Neural Network
Conference NameConference on Optics in Atmospheric Propagation and Adaptive Systems XIX
Conference DateSEP 28-29, 2016
Conference PlaceEdinburgh, SCOTLAND
Indexed ByEI ; CPCI
Document Type会议论文
Identifierhttp://ir.nssc.ac.cn/handle/122/5799
Collection空间技术部
Corresponding AuthorMa, XS (reprint author), Chinese Acad Sci, Natl Space Sci Ctr, Key Lab Elect & Informat Technol Space Syst, Beijing 100190, Peoples R China.
Recommended Citation
GB/T 7714
Ma, Xiaoshan,Wang, Haidong,Li, Ligang,et al. Neural network simulation of the atmospheric point spread function for the adjacency effect research[C],2016.
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