Machine-learning-assisted Analysis Of Polarimetric Scattering From Cylindrical Components Of Vegetation

Vegetation is the most important category of land object research.Among planets in the solar system,this is one of the most important characteristics of the Earth.It is about the survival and economic development of mankind.Therefore,it is of great research value to study the relationship between the structure type of vegetation and the surrounding environment.Reliable and efficient analysis of electromagnetic scattering by cylindrical components of vegetation is important for microwave remote sensing of vegetated terrain.In this paper,a deep neural network(DNN)architecture is adopted in the hope that with increased depth of the neural network hence increased abstraction capability,it may be able to handle the highly oscillatory scattering patterns to an adequately acceptable degree.The scheme has demonstrated the capability of modifying and adapting itself to capture the complicated polarimetric bistatic scattering patterns of a finite dielectric cylinder.The physical consideration of reciprocity relation is largely fulfilled except for the scattered directions close to the cylinder axis.More and more importantly,for cases where interpolation is expected,the scheme has unambiguously demonstrated the capability of learning the bistatic scattering cross section and phase patterns.The performance is also robust against the number of parameters to be interpolated,be it single or multiple.In summary,the proposed machine learning scheme bodes well for the design of future physically based algorithms where conventionally data-cube was used as the base for interpolation.