Generalized-Gaussian-Rician Model for SAR Images

In this project, we present a novel statistical model, the generalized-Gaussian-Rician (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. In various SAR scenes, the illuminated area may include one (or a small number of) dominating scatterer(s), and a large number of non-dominant ones. Hence, the in-phase and quadrature components of the back-scattered SAR signal become statistically iid, but non-zero-mean random variables.

No dominating scatterer (Rayleigh case)
Hybrid case (Rician case)

This idea motivates us to utilise Rician distribution whilst modelling the SAR amplitude. Therefore, 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 GG-Rician statistical model is a general statistical model, which covers various important amplitude and intensity statistical models as special members.

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 state-of-the-art statistical models that include

Weibull, G0, Generalized gamma, and the lognormal distribution.

The statistical significance analysis and goodness of fit test results demonstrate the superior performance and flexibility of the proposed model for all frequency bands and scenes, and its applicability on both amplitude and intensity SAR images.

Further information

[1] Karakuş, O., Kuruoglu, E. E., & Achim, A. (2020). A Generalized Gaussian Extension to the Rician Distribution for SAR Image ModelingarXiv preprint arXiv:2006.08300.

[2] Karakuş, O., Kuruoğlu, E. E., & Achim, A. (2020, May). Modelling sea clutter in SAR images using Laplace-Rician distribution. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1454-1458). IEEE.

[3] Karakuş, O., Kuruoglu, E. E., & Achim, A. (2020). A Modification of Rician Distribution for SAR Image Modelling.


Oktay Karakuş, Ercan E. Kuruoglu, Alin Achim


This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/R009260/1 (AssenSAR).