Optimum edge detection in SAR

Oliver, C.J.; Blacknell, D.; White, R. G.

doi:10.1049/ip-rsn:19960219

Cited by 112 publications

(42 citation statements)

References 4 publications

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“…The classical gradient by difference is thus not a constant false alarm rate operator. Statistical studies [32], [33], [34] have shown that the use of ratio is more suitable to multiplicative noise than the use of difference. Several edge detectors using ratio have been introduced in order to obtain a constant false alarm rate on SAR images:…”

Section: A Gradient Computation For Sar Images 1) State Of the Artmentioning

confidence: 99%

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SAR-SIFT: A SIFT-Like Algorithm for SAR Images

Dellinger

Delon

Gousseau

et al. 2015

IEEE Trans. Geosci. Remote Sensing

500

253

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Abstract-The Scale Invariant Feature Transform (SIFT) algorithm is widely used in computer vision to match features between images or to localize and recognize objets. However, mostly because of speckle noise, it does not perform well on synthetic aperture radar (SAR) images. We present here an improvement of this algorithm for SAR images, named SAR-SIFT. A new gradient computation, yielding an orientation and a magnitude robust to speckle noise, is first introduced. It is then used to adapt several steps of the SIFT algorithm to SAR images. We study the improvement brought by this new algorithm, compared to existing approaches. We present an application of SAR-SIFT for the registration of SAR images in different configurations, especially with different incidence angles.

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Section: A Gradient Computation For Sar Images 1) State Of the Artmentioning

confidence: 99%

“…All images have a size of 512 × 512 pixels and have been all acquired under the same viewing conditions (34 • incidence angle, 2m resolution, SpotLight mode). A visual check showed that no temporal changes occur between the two images of each pair, so that only the noise realisation differs.…”

Section: A Test Images and Parametersmentioning

confidence: 99%

SAR-SIFT: A SIFT-Like Algorithm for SAR Images

Dellinger

Delon

Gousseau

et al. 2015

IEEE Trans. Geosci. Remote Sensing

500

253

View full text Add to dashboard Cite

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“…The Lambertian model, generally used in the photoclinometry [334], assumes a random rough and statistically uniform surface yielding the image intensity proportional to the cosine of the local tilt angle. However, this model is not generally applicable to SAR data, and various distribution functions have been used to model SAR image statistics, including Gaussian [329,337], Rayleigh [338], Gamma [339,340], and Rayleigh-Bessel composite [332] distributions (see also [238]). Radarclinometry is simple and easy to implement, but the technique is considered as a qualitative relief reconstruction method for its accuracy is marred by large errors caused by the geometric effect of foreshortening and speckle noise [333].…”

Section: Other Land Featuresmentioning

confidence: 99%

Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics

2013

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The present article is an introductory paper in this special issue on synthetic aperture radar (SAR). A short review is presented on the recent trend and development of SAR and related techniques with selected topics, including the fields of applications, specifications of airborne and spaceborne SARs, and information contents in and interpretations of amplitude data, interferometric SAR (InSAR) data, and polarimetric SAR (PolSAR) data. The review is by no means extensive, and as such only brief summaries of of each selected topics and key references are provided. For further details, the readers are recommended to read the literature given in the references theirin.

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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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Optimum edge detection in SAR

Cited by 112 publications

References 4 publications

SAR-SIFT: A SIFT-Like Algorithm for SAR Images

SAR-SIFT: A SIFT-Like Algorithm for SAR Images

Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics

Edge detection in SAR images using phase stretch transform

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