Image and Signal Processing Methods

Adaptive method of speckle reduction based on curvelet transform and thresholding neural network in synthetic aperture radar images

[+] Author Affiliations
Fatemeh Zakeri

Tehran University, 2 Schools of Technical College, Department of Geomatics Engineering, North Kargar Street, After Jalal Ale Ahmad, Tehran, Iran

K.N. Toosi University of Technology, Department of Geomatics Engineering, No. 1346, ValiAsr Street, Mirdamad Cross, Tehran, Iran

Mohammad Javad Valadan Zoej

K.N. Toosi University of Technology, Department of Geomatics Engineering, No. 1346, ValiAsr Street, Mirdamad Cross, Tehran, Iran

J. Appl. Remote Sens. 9(1), 095043 (Nov 20, 2015). doi:10.1117/1.JRS.9.095043
History: Received August 14, 2014; Accepted October 21, 2015
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Abstract.  Because of the effect of speckles in synthetic aperture radar (SAR) images, its reduction has been considered by many researchers to obtain reliable information. This paper proposes a method based on the curvelet transform to reduce speckles in SAR images. This study is based on the thresholding neural network (TNN) technique, which has been previously used in wavelet transformation. In addition, an adaptive learning TNN with remarkable time saving was introduced. Comparing the obtained results from the method with conventional speckle filters such as Lee, Kuan, Frost, and Gamma filters, curvelet-based, nonadaptive despeckling, wavelet-based TNN despeckling, and curvelet-based particle swarm optimization show better achievement of the proposed algorithm. For instance, noise mean value, noise standard deviation, mean square difference, equivalent number of looks, and β (an edge-preserving criterion) improved 2%, 9%, 21%, 35%, and 9%, respectively.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Fatemeh Zakeri and Mohammad Javad Valadan Zoej
"Adaptive method of speckle reduction based on curvelet transform and thresholding neural network in synthetic aperture radar images", J. Appl. Remote Sens. 9(1), 095043 (Nov 20, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.095043


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