Stepwise-Regression-Based Finite Mixture Model for Multi-Aspect SAR Image Modeling

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.