Target Scattering Feature Extraction Based on Parametric Model Using Multi-Aspect SAR Data

Abstract

The multi-aspect SAR observation can obtain the backscattering information of the illuminated scene target. There are targets with different structures in the scene, and their backscattering responses are also different. Using backscattering amplitude information to analyze the differences between targets is a conventional method. For point target types, one-dimensional backscattering curves can be used to analyze scattering characteristics, but it is difficult to analyze the overall structure of the target. Therefore, it is necessary to perform statistical analysis on the backscattering information combinating with the multi-aspect target area, and establish parameters to model the target area. In this paper, the algorithm uses the G0 distribution based on expectation maximization (EM) to fit the target area of the SAR scene. For different target types in the scene, the β and σ parameters obtained by the model combined with the backscattering amplitude information are used to perform the target. The results show that full-target in multi-aspect SAR image can be differentiated by two parameters. The scattering of partial-target slices can be characterized using two parameters (amplitude difference from surrounding points, scattering energy). The parametric model quantitatively characterizes the scattering feature of the target level, and the parameters changing corresponds to the change of the target image feature. C-band circular SAR data is used to validate our method. The experimental results give the parameter representation with sampling window based on the analysis of the target scattering, and give parameter estimates to characterize the partial target scattering.