TL;DR
This paper introduces the GG-Rician distribution, a new statistical model for SAR images that improves modeling accuracy across various scenes and frequency bands, enhancing applications like target tracking and despeckling.
Contribution
The paper proposes the generalized-Gaussian-Rician distribution, extending the Rician model with generalized-Gaussian components for better SAR image characterization.
Findings
The GG-Rician model outperforms existing models like K, Weibull, Gamma, and Lognormal.
Statistical tests confirm the superior fit of the GG-Rician model across diverse scenes.
The model is applicable to both amplitude and intensity SAR images.
Abstract
In this paper, we present a novel statistical model, (GG-Rician) distribution, for the characterization of synthetic aperture radar (SAR) images. Since accurate statistical models lead to better results in applications such as target tracking, classification, or despeckling, characterizing SAR images of various scenes including urban, sea surface, or agricultural, is essential. The proposed statistical model is based on the Rician distribution to model the amplitude of a complex SAR signal, the in-phase and quadrature components of which are assumed to be generalized-Gaussian distributed. The proposed amplitude GG-Rician model is further extended to cover the intensity SAR signals. In the experimental analysis, the GG-Rician model is investigated for amplitude and intensity SAR images of various frequency bands and scenes in comparison to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
