基于邻域约束的大气偏振模式生成网络  

A generative method for atmospheric polarization modelling based on neighborhood constraint

作　　者：程前 高欣健 高隽[1,2] 王昕 党天一[1,2] 严圆 Cheng Qian;Gao Xinjian;Gao Jun;Wang Xin;Dang Tianyi;Yan Yuan(School of Computer and Information,Hefei University of Technology,Hefei,Anhui 230009,China;Image Information Processing Laboratory,Hefei University of Technology,Hefei,Anhui 230009,China)

机构地区：[1]合肥工业大学计算机与信息学院,安徽合肥230009 [2]合肥工业大学图像信息处理研究室,安徽合肥230009

出　　处：《光电工程》2022年第6期53-67,共15页Opto-Electronic Engineering

基　　金：国家自然科学基金面上项目(62171178)。

摘　　要：大气偏振模式凭借具有太阳子午线信息的“∞”字形特征支撑偏振导航应用,然而由于采集装置的物理特性限制、采集地点的周边环境以及薄云等遮挡,导致获取的大气偏振信息部分失真,降低了太阳子午线的精度。为解决该问题,本文提出了基于邻域约束的大气偏振模式生成网络,该网络挖掘大气偏振模式分布的连续性,通过多步邻域特征推理以增加重构过程的约束,由局部有效偏振信息精准生成全局的大气偏振信息。此外,针对大气偏振模式的物理特性,提出了太阳子午线角度损失,进一步提升太阳子午线精度。本文在实测大气偏振数据上进行了实验,并与其它最新方法进行对比,实验结果证明了本文方法的鲁棒性和优越性。Atmospheric polarization mode supports the polarization navigation application by virtue of the"∞"feature containing the solar meridian information.However,due to the limitation of the physical characteristics of the acquisition device,the surrounding environment of the acquisition location and the occlusion of thin clouds,the obtained atmospheric polarization information is partially distorted and the accuracy of the solar meridian is reduced.In order to solve this problem,this paper proposes an atmospheric polarization pattern generation network based on neighborhood constraints.The network mines the continuity of atmospheric polarization pattern distribution,increases the constraints of reconstruction process through multi-step neighborhood feature reasoning,and accurately generates global atmospheric polarization information from local effective polarization information.In addition,according to the physical characteristics of the atmospheric polarization mode,the angle loss of solar meridian is proposed to further improve the accuracy of the solar meridian.In this paper,experiments are carried out on the measured atmospheric polarization data,and compared with other latest methods.The experimental results show the robustness and superiority of this method.

关 键 词：大气偏振模式 偏振信息重构 邻域特征推理 子午线角度损失 深度学习 

分 类 号：O436.3[机械工程—光学工程] TP391.4[理学—光学]