Noise models for linear feature detection in SAR images

Author

Evans, Adrian N. ; Sharp, Nigel G. ; Hancock, Edwin R.

Author_Institution

Dept. of Comput. Sci., York Univ., UK

Volume

1

fYear

1994

fDate

13-16 Nov 1994

Firstpage

466

Abstract

The aim is to present a model capable of capturing the statistical correlations introduced by multiplicative noise effects in SAR image data. The motivation behind the study is the need to model noise statistics so that a Bayesian relaxation scheme may be applied to the detection of features in SAR images. The authors present a model which predicts the edge or line gradient distributions to be a product of Rayleigh and Bessel function components; this factorisation separates the correlated and uncorrelated components of the noise

Keywords

Bayes methods; Bessel functions; correlation methods; edge detection; feature extraction; radar imaging; radar interference; radiofrequency interference; synthetic aperture radar; Bayesian relaxation scheme; Bessel function components; Rayleigh function component; SAR images; correlated components; edge gradient distributions; factorisation; line gradient distributions; linear feature detection; multiplicative noise effects; noise statistics; statistical correlations; uncorrelated components; Additive noise; Bayesian methods; Computer vision; Filtering; Filters; Signal to noise ratio; Speckle; Statistical distributions; Statistics; Synthetic aperture radar;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference

Conference_Location

Austin, TX

Print_ISBN

0-8186-6952-7

Type

conf

DOI

10.1109/ICIP.1994.413357

Filename

413357