Multi-aspect synthetic aperture radar (SAR) is an emerging observation mode in SAR. Through multi-aspect observations, along with coherent and incoherent image processing, multi-aspect SAR effectively addresses issues like layovers, shadows, and foreshortening in conventional SAR. It can obtain multi-aspect scattering images of the observed scene. Modeling the statistical distribution characteristics of multi-aspect SAR images is crucial for its processing and applications. Currently, there is no comprehensive and systematic study on the statistical distribution characteristics of multi-aspect SAR images. Therefore, this paper conducts qualitative and quantitative analyses of these characteristics. Furthermore, we investigate the applicability and limitations of five single-parametric models commonly used in conventional SAR for modeling the statistical distribution characteristics of multi-aspect SAR images. The experimental results show that none of these models could accurately model the multi-aspect SAR images. To address this issue, we propose a finite mixture model (FMM) and evaluate its feasibility to accurately model the statistical distribution characteristics of multi-aspect SAR on X-band GOTCHA data and C-band Zhuhai data. The experimental results demonstrate that, compared with the single-parametric models, our method can accurately model the statistical distribution characteristics of various types of targets in multi-aspect SAR images from different observation aspects and aperture angles in various bands.

Compared with conventional synthetic aperture radar (SAR), multi-aspect SAR can observe a scene from various aspects, thus providing a more detailed and comprehensive analysis and description of the target. As a result, an accurate, stable, and efficient model is required to adaptively model the multi-aspect SAR images according to the precision requirements. To address this challenge, we propose a stepwise-regression-based finite mixture model (SRFMM), with the aim of constructing a finite mixture model (FMM) by combining the fewest single parametric models that meet a specified accuracy demand. The SRFMM first employs a voting-based ranking strategy to determine the order in which the single parametric models are added to the FMM. And then, it linearly combines single parametric models one by one in the determined order until the desired accuracy is achieved or overfitting occurs to obtain the final FMM. In the implementation of SRFMM, we employ the particle swarm optimization (PSO) algorithm for parameter and coefficient estimation due to its robustness and parallelism. We have conducted an experimental evaluation of the SRFMM using the C-band circular SAR (CSAR) data, and the results indicated that the SRFMM can accurately, stably, and efficiently model the isotropic and anisotropic regions in multi-aspect SAR images under various observation aspects and aperture angles. Evaluation on the X-band CSAR data also indicates the applicability of the SRFMM.