| 题名: | Neural network simulation of the atmospheric point spread function for the adjacency effect research |
| 作者: | Ma, Xiaoshan; Wang, Haidong; Li, Ligang; Yang, Zhen; Meng, Xin
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| 作者部门: | 空间技术部
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| 通讯作者: | Ma, XS (reprint author), Chinese Acad Sci, Natl Space Sci Ctr, Key Lab Elect & Informat Technol Space Syst, Beijing 100190, Peoples R China.
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| 会议名称: | Conference on Optics in Atmospheric Propagation and Adaptive Systems XIX
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| 会议日期: | SEP 28-29, 2016
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| 会议地点: | Edinburgh, SCOTLAND
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| 会议录: | OPTICS IN ATMOSPHERIC PROPAGATION AND ADAPTIVE SYSTEMS XIX
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| 出版日期: | 2016
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| 收录类别: | EI
; CPCI
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| ISSN号: | 0277-786X
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| ISBN号: | 978-1-5106-0408-7; 978-1-5106-0409-4
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| 关键词: | adjacency effect
; atmospheric point spread function
; Monte Carlo simulation
; neural network
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| 英文摘要: | Adjacency 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. |
| 语种: | 英语
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| 内容类型: | 会议论文
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| URI标识: | http://ir.nssc.ac.cn/handle/122/5799
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| Appears in Collections: | 空间技术部_会议论文
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| 2016100020D.pdf（593KB） | 会议论文 | -- | 限制开放 | | View
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| Recommended Citation: | |
Ma, Xiaoshan,Wang, Haidong,Li, Ligang,et al. Neural network simulation of the atmospheric point spread function for the adjacency effect research[C]. 见:Conference on Optics in Atmospheric Propagation and Adaptive Systems XIX. Edinburgh, SCOTLAND. SEP 28-29, 2016.
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