Edge Enhancement Algorithm Based on the Wavelet Transform for Automatic Edge Detection in SAR Images

Alonso, Marivi Tello; López-Martínez, Carlos; Mallorquí, J.J.; Salembier, Philippe

doi:10.1109/tgrs.2010.2052814

Cited by 115 publications

(23 citation statements)

References 23 publications

(25 reference statements)

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“…The method proposed in this paper is based on a different way of manipulating multiscale data [14]. It does not assume any statistical distribution of the input data and any particular type of edge.…”

Section: Edge Detection Methodsmentioning

confidence: 99%

“…It works exclusively in the transformed domain. Several algorithms are based on the wavelet theory like the study in [14] or [15] proposes an edge detector based on a threshold operation of wavelet coefficients. Despite a low computational cost and a good contrast, detected edges are too thick.…”

Section: Edge Detection Methodsmentioning

confidence: 99%

“…If a feature (i.e., a vessel) is present in a particular subband, the histogram shall be heavy tailed, and the normalization is performed as usual using the maximum of the corresponding subband. If not, the normalization factor is a number of times (usually three) the standard deviation in order to ensure low values on this subband [14]. Since, the components in the same scale are uncorrelated, taking the maximum value pixel per pixel permits avoiding as much as possible the speckle.…”

Section: Normalizationmentioning

confidence: 99%

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Enhanced Edge Detection Technique for SAR Images

SuryaPrasad¹,

Rao²,

Chandrasekhar³

2013

IJCA

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In this work a novel technique for automatic edge enhancement and detection in synthetic aperture radar (SAR) images is proposed. The edge enhancement phase has been proven to be critical in heterogeneous SAR images, and the method proposed is a good solution that can be used to deal with this type of data. The method proposes a robust edge enhancement directly in the wavelet transformed domain based on a combination of wavelet coefficients at different scales. It does not require any type of pre filtering of input data, and is independent of the statistics of the input image. The adaptation capability of the method to very diverse scenarios with no need of a priori knowledge or settings is a useful feature in view of its integration in an unsupervised segmentation. General TermsImage Processing.

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Section: Edge Detection Methodsmentioning

confidence: 99%

Section: Edge Detection Methodsmentioning

confidence: 99%

Section: Normalizationmentioning

confidence: 99%

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Enhanced Edge Detection Technique for SAR Images

SuryaPrasad¹,

Rao²,

Chandrasekhar³

2013

IJCA

View full text Add to dashboard Cite

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“…O ir " xT ir (2) where O ir is the observed intensity ratio, x is a multiplicative speckle contribution, and T ir is the underlying true intensity ratio. The observed intensity ratio image has the disadvantage that the multiplicative noise is difficult to remove.…”

Section: Logarithmic Scaling and Ratio Image Formationmentioning

confidence: 99%

“…Multi-temporal images acquired by optical [1] or radar remote sensing sensors [2] are routinely applied to the detection of changes on the Earth's surface. As both of these two sensor types have their own imaging and sensitivity characteristics, their performance in change detection varies with the properties of the changing surface features [3].…”

Section: Introductionmentioning

confidence: 99%

Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach

2016

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Despite the significant progress that was achieved throughout the recent years, to this day, automatic change detection and classification from synthetic aperture radar (SAR) images remains a difficult task. This is, in large part, due to (a) the high level of speckle noise that is inherent to SAR data; (b) the complex scattering response of SAR even for rather homogeneous targets; (c) the low temporal sampling that is often achieved with SAR systems, since sequential images do not always have the same radar geometry (incident angle, orbit path, etc.); and (d) the typically limited performance of SAR in delineating the exact boundary of changed regions. With this paper we present a promising change detection method that utilizes SAR images and provides solutions for these previously mentioned difficulties. We will show that the presented approach enables automatic and high-performance change detection across a wide range of spatial scales (resolution levels). The developed method follows a three-step approach of (i) initial pre-processing; (ii) data enhancement/filtering; and (iii) wavelet-based, multi-scale change detection. The stand-alone property of our approach is the high flexibility in applying the change detection approach to a wide range of change detection problems. The performance of the developed approach is demonstrated using synthetic data as well as a real-data application to wildfire progression near Fairbanks, Alaska.

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