A statistical and geometrical edge detector for SAR images

Touzi, R.; Lopès, A.; Bousquet, P.

doi:10.1109/36.7708

Cited by 590 publications

(309 citation statements)

References 4 publications

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“…Generally, when the surface reflectivity is known, the probability of the observed intensity for an looks intensity image is of the form (15) Normally, however, the speckle random process is normalized, which yields a random process mean having the form (16) This normalization leads to a multiplicative model of the form (17) This model, valid for homogeneous low-texture surfaces, entails the following relations established between the standard deviations of the surface reflectivity, speckle, and intensity:…”

Section: A Multiplicative Modelmentioning

confidence: 99%

Contribution of the fractal dimension to multiscale adaptive filtering of SAR imagery

Germain¹,

Bénié

Boucher³

et al. 2003

IEEE Trans. Geosci. Remote Sensing

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Abstract-Radar images can show great variability from pixel to pixel, which is an obstacle to effective processing. This variability, due to speckle created by the radar wave coherence, necessitates the use of more adapted filters. Previous studies have shown that multiresolution wavelet analysis yields better results but produces artefacts due to multiscale decomposition. This paper proposes a method that reduces these effects by introducing the fractal dimension. The resultant filter combines wavelet decomposition and variance change model based on the level of variance estimated by studying the fractal dimension of the image.Index Terms-Adaptive filtering, fractal dimension, multiresolution analysis, synthetic aperture radar (SAR), speckle effect, wavelet transform.

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Section: A Multiplicative Modelmentioning

confidence: 99%

Contribution of the fractal dimension to multiscale adaptive filtering of SAR imagery

Germain¹,

Bénié

Boucher³

et al. 2003

IEEE Trans. Geosci. Remote Sensing

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“…Thus it is required for more specialised methods to be used. Recent edge detection techniques can be classified in three main categories: methods based on the ratio of averages (ROA) of image intensities [6], methods based on Likelihood Ratio (LR) [7] and methods based on wavelet analysis [8]. In this work a physics-inspired digital image transformation is employed that emulates the propagation of electromagnetic waves through a diffractive medium with a dielectric function that has warped dispersive (frequency dependent) property [9].…”

Section: Introductionmentioning

confidence: 99%

Edge detection in SAR images using phase stretch transform

Ilioudis¹,

Clemente²,

Asghari³

et al. 2015

2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)

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In this work a novel approach to edge detection on Synthetic Aperture Radar (SAR) images is introduced. The proposed method uses an optics inspired transform which emulates the diffraction of an image through a medium with nonlinear dispersive properties. The experimental results show that the output of the introduced Phase Stretch Transform (PST) in conjunction with further morphological operations can be effectively used for image edge detection.

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“…For the real aerial image, most of the related research recognized the power line after the liner object enhancement or edge extraction. Reference [3] proposed a power transmission line detection methods, they use the method proposed in [4][5] to extract the linear object, then detect the power line in each sub-block and connect them by kalman filter. Li et al [1] proposed a toward automatic power line detection for surveillance system using pulse coupled neural filter and Hough transform.…”

Section: Introductionmentioning

confidence: 99%

Power line detection based on symmetric partial derivative distribution prior

Cao

Yang

Zhu

et al. 2013

2013 IEEE International Conference on Information and Automation (ICIA)

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-In this paper, we propose a simple but effective image prior--symmetry partial derivative distribution to detect power lines in aerial image for UAVs. The symmetry partial derivative distribution is a kind of statistics of the images. It is based on a key observation-most nature images have symmetry partial derivative distributing. Based on this prior knowledge, we use radon transformation in partial derivative image and recognize the power lines in the aerial image. The experiment results demonstrate our method is effective for automatic power line detection.

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A statistical and geometrical edge detector for SAR images

Cited by 590 publications

References 4 publications

Contribution of the fractal dimension to multiscale adaptive filtering of SAR imagery

Contribution of the fractal dimension to multiscale adaptive filtering of SAR imagery

Edge detection in SAR images using phase stretch transform

Power line detection based on symmetric partial derivative distribution prior

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