A few-shot learning based generative method for atmospheric polarization modelling

Gan Xin; Gao Xinjian; Zhong Binbin; Wang Xin; Ye Zirui; Gao Jun

doi:10.12086/oee.2021.200331

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Gan X, Gao X J, Zhong B B, et al. A few-shot learning based generative method for atmospheric polarization modelling[J].Opto-Electron Eng, 2021, 48(5): 200331. doi: 10.12086/oee.2021.200331

Citation:

Gan X, Gao X J, Zhong B B, et al. A few-shot learning based generative method for atmospheric polarization modelling[J]. Opto-Electron Eng, 2021, 48(5): 200331. doi: 10.12086/oee.2021.200331

A few-shot learning based generative method for atmospheric polarization modelling

1.
School of Computer and Information, Hefei University of Technology, Hefei, Anhui 230009, China
2.
Image Information Processing Laboratory, Hefei University of Technology, Hefei, Anhui 230009, China
3.
Hefei GaoChuang Joint Stock Limited Company, Hefei, Anhui 230088, China

Fund Project: National Natural Science Foundation of China (61971177, 61806066)

More Information

Corresponding author: Gao Xinjian, E-mail: gaoxinjian@hfut.edu.cn

Received Date 07 September 2020

Revised Date 30 March 2021

Published Date 15 May 2021

Abstract

Abstract

Atmospheric polarization has broad application prospects in navigation and other fields. However, due to the limitation of the physical characteristics of the atmospheric polarization information acquisition device, only local and discontinuous polarization information can be obtained at the same time, which has an impact on the practical application. In order to solve this problem, by mining the continuity of atmospheric polarization mode distribution, this paper proposes a network for generating atmospheric polarization mode from local polarization information. In addition, polarization information is often affected by different weather conditions, geographic environment and other factors, and these polarization data are difficult to collect in the real environment. To solve this problem, this paper mines the diversity relationship between the few-shot data under different weather and geographic conditions, by which the generated atmospheric polarization mode is generalized to different conditions. In this paper, experiments are carried out on the simulated data and measured data. Compared with other new methods, the experimental results prove the superiority and robustness of this proposed method.
- atmospheric polarization mode generation /
- few-shot driven /
- polarization data mining

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References

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Overview

Overview

Overview: As an earth's natural attribute, atmospheric polarization pattern contains rich information of the distribution of the optical properties and stability of the atmospheric polarization pattern in the sky. Even under complex atmospheric environment factors, the atmospheric polarization pattern still presents a series of continuous distribution of time and space. Therefore, in the autonomous navigation, target detection and other fields, it has broad application prospects. However, due to the limitation of the physical characteristics of the atmospheric polarization information acquisition device, only local and discontinuous atmospheric polarization information can be obtained at the same time, and it is difficult to form the atmospheric polarization mode in the whole sky, which will affect the practical application. To solve this problem, this paper proposes a polarization pattern generation network by mining the continuity of the atmospheric polarization pattern distribution. Using this continuous distribution, atmospheric polarization pattern is generated by inverse transformation sampling. Then the generated atmospheric polarization mode is compared with the real atmospheric polarization mode to correct the previously mined continuous distribution characteristics and inverse transformation sampling process. Through continuous iterative optimization, the essential distribution characteristics among the local atmospheric polarization information are finally excavated, and the atmospheric polarization mode model is completed. The missing information is supplemented by the model, and the all-sky atmospheric polarization mode is generated from the local atmospheric polarization information. In addition, atmospheric polarization information is often affected by different weather conditions, geographic environment and other factors, and these polarization data are difficult to collect in the real environment. To solve this problem, this paper excavates the diversity relationship between the few-shot sample data under different weather and geographic conditions, and generalizes the generated atmospheric polarization mode to different conditions based on the relationship. For the above two problems, this paper designs a drive atmospheric polarization pattern generation network based on the few-shot data, which is obtained by digging through the local atmospheric polarization pattern under different weather, geographical locations, and continuity of polarization diversity of the relationship between different data, and which is also based on the formation of polarization information, and the generalization ability to generate the atmospheric polarization pattern under different conditions. In this paper, the simulated data and measured data are used to carry out experiments. Compared with other latest methods, the experimental results prove the superiority and robustness of this proposed method. In addition, the ablation experiment also proves that the network designed in this paper has the generalization ability and better robustness.