An improved detection and feature retrieval method of anisotropic scattering for multi-aspect PolSAR data processing based on DRIA framework

Multi-aspect PolSAR data contains polarimetric properties from different look angle. Multi-aspect polarimetric information can be applied in geometric measurement, target identifying, precise classification. In order to characterize anisotropic target, anisotropic and isotropic scattering need to be separated from the raw data. A detecting-removing-incoherent-adding (DRIA) framework, presented in Li Yang's doctoral dissertation, suggests to remove the anisotropic scattering, gain a removal series and incoherent integrate the reserved data. In this paper, in order to identify anisotropic target, an anisotropic scattering model is raised. An improved detection and feature retrieval method is presented base on DRIA framework. The equivalent number of looks (ENL) used in Li Yang's dissertation is proved to bring measurement error to the result. The anisotropic scattering can be correctly identified after the error is restored. Two kinds of maximum-likelihood ratio are proved to gain the same result in sort. Three features are retrieved from the removal series to describe the anisotropic scattering. The experimental data is circular SAR (CSAR) data acquired by the Institute of Electronics airborne CSAR system at P-band.