Analysis and Modeling of Statistical Distribution Characteristics for Multi-Aspect SAR Images
Year: 2025
Type: article
Abstract: 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.
Source: Remote Sensing
DOI: 10.3390/rs17071295
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W4409208193