Noise models for linear feature detection in SAR images

A. N. Evans; N. G. Sharp; E. R. Hancock

doi:10.1109/ICIP.1994.413357

Noise models for linear feature detection in SAR images

A. N. Evans, N. G. Sharp, E. R. Hancock

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Article number	413357
Pages (from-to)	466-470
Number of pages	5
Journal	Proceedings - International Conference on Image Processing, ICIP
Volume	1
Issue number	410
DOIs	https://doi.org/10.1109/ICIP.1994.413357
Publication status	Published - 1 Oct 1994
Event	The 1994 1st IEEE International Conference on Image Processing - Austin, TX, USA Duration: 13 Nov 1994 → 16 Nov 1994

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

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10.1109/ICIP.1994.413357

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title = "Noise models for linear feature detection in SAR images",

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.",

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AU - Sharp, N. G.

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N2 - 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.

AB - 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.

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U2 - 10.1109/ICIP.1994.413357

DO - 10.1109/ICIP.1994.413357

M3 - Conference article

AN - SCOPUS:84996587722

SN - 1522-4880

VL - 1

SP - 466

EP - 470

JO - Proceedings - International Conference on Image Processing, ICIP

JF - Proceedings - International Conference on Image Processing, ICIP

IS - 410

M1 - 413357

T2 - The 1994 1st IEEE International Conference on Image Processing

Y2 - 13 November 1994 through 16 November 1994

ER -

Noise models for linear feature detection in SAR images

Abstract

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