激光与光电子学进展, 2016, 53 (10): 102801, 网络出版: 2016-10-12
人工神经网络计算大气点扩展函数
Computing the Atmospheric Point Spread Function by Artificial Neural Networks
遥感 邻近效应 大气点扩展函数 逆向蒙特卡罗法 人工神经网络 remote sensing adjacency effect atmospheric point spread function backward Monte Carlo method artificial neural network
摘要
大气点扩展函数(PSF)是开展光学遥感邻近效应研究和校正的有效方法。基于蒙特卡罗模拟获得的大气PSF,设计足够多带有Sigmoid函数的隐藏神经元和线性输出神经元的两层前馈神经网络,采用Levenberg-Marquardt反向传播算法,获得了大气、光谱和观测几何等输入参数与大气PSF之间的关系。模拟结果证明该方法能够在相对较短的时间内,以95%的计算精度产生预期的大气PSF的近似值。
Abstract
Atmospheric point spread function (PSF) is an effective research and correction method for the adjacency effect of the optical remote sensing. Based on the atmospheric PSF acquired by Monte Carlo simulation, a two-layer feed-forward neural network which has enough hidden neurons with Sigmoid function and linear output neurons is designed and implemented. By means of Levenberg-Marquardt back-propagation algorithm, the relationship between the atmospheric PSF and its influence factors, such as atmosphere condition, spectral range and observation geometry is obtained. The results obtained show that our neural network can estimate the atmosphere PSF with 95% accuracy within relatively short time.
王海东, 马晓珊, 杨震, 李立钢. 人工神经网络计算大气点扩展函数[J]. 激光与光电子学进展, 2016, 53(10): 102801. Wang Haidong, Ma Xiaoshan, Yang Zhen, Li Ligang. Computing the Atmospheric Point Spread Function by Artificial Neural Networks[J]. Laser & Optoelectronics Progress, 2016, 53(10): 102801.