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

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.