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Compared with the two-dimensional (2D) attributed scattering center model (ASCM), three dimensional (3D) fully polarimetric attributed scattering center model (FP-ASCM) is able to directly offer the target location, size, and orientation in 3D space, which plays an important role in noncooperative target recognition. Considering that 3D images of the target are hard to obtain, 3D fully polarimetric attributed scattering center feature extraction from sequential 2D SAR images is addressed, and a novel method is proposed based on sparse representation in a frequency-azimuth-elevation domain. The proposed method consists of two steps: scattering center associations from sequential images and 3D FP-ASCM parameter estimation. First, a new scattering center association method is proposed by using epipolar geometry and polarization characteristics. In this method, scattering centers from different images can be associated by minimizing their projection distance and polarimetric diversity. Second, 3D fully polarimetric attributed scattering center feature is extracted based on sparse representation in a frequency-azimuth-elevation domain. As 3D FP-ASCM includes eight target parameters and three radar parameters, the dimension of the parameterized dictionary is very large. Thus, parameter initialization and range-orientation uncoupling are adopted to reduce the dimension of the parameterized dictionary. The numerical results for electromagnetic computation data and measurement data show the efficiency of the proposed method.

3D Fully Polarimetric Attributed Scattering Center Extraction Based on Sequential 2D SAR Images