Cauchy-Rician model for backscattering in urban SAR images

Karakus, Oktay ORCID: https://orcid.org/0000-0001-8009-9319, Kuruoglu, Ercan E., Achim, Alin M. and Altinkaya, Mustafa A. 2022. Cauchy-Rician model for backscattering in urban SAR images. IEEE Geoscience and Remote Sensing Letters 19 , 4504905. 10.1109/LGRS.2022.3146370

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Abstract

This paper presents a new statistical model for urban scene SAR images by combining the Cauchy distribution, which is heavy-tailed, with the Rician back-scattering. The literature spans various well-known models most of which are derived under the assumption that the scene consists of multitudes of random reflectors. This idea specifically fails for urban scenes since they accommodate a heterogeneous collection of strong scatterers such as buildings, cars, wall corners. Moreover, when it comes to analysing their statistical behaviour, due to these strong reflectors, urban scenes include a high number of high amplitude samples, which implies that urban scenes are mostly heavy-tailed. The proposed Cauchy-Rician model contributes to the literature by leveraging non-zero location (Rician) heavy-tailed (Cauchy) signal components. In the experimental analysis, the Cauchy-Rician model is investigated in comparison to state-of-the-art statistical models that include G0, generalized gamma, and the lognormal distribution. The numerical analysis demonstrates the superior performance and flexibility of the proposed distribution for modelling urban scenes.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > Q Science (General) T Technology > TA Engineering (General). Civil engineering (General)
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1545-598X
Funders:	Engineering and Physical Sciences Research Council (Grant Number: EP/R009260/1)
Date of First Compliant Deposit:	25 February 2022
Date of Acceptance:	26 January 2022
Last Modified:	03 Dec 2024 06:45
URI:	https://orca.cardiff.ac.uk/id/eprint/147747

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