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Remote Sens. 2014, 6(6), 5497-5519; doi:10.3390/rs6065497

Synthetic Aperture Radar Image Clustering with Curvelet Subband Gauss Distribution Parameters

Department of Computer Engineering, Yildiz Technical University, 34220 Istanbul, Turkey
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Received: 27 February 2014 / Revised: 29 May 2014 / Accepted: 30 May 2014 / Published: 16 June 2014
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Abstract

Curvelet transform is a multidirectional multiscale transform that enables sparse representations for signals. Curvelet-based feature extraction for Synthetic Aperture Radar (SAR) naturally enables utilizing spatial locality; the use of curvelet-based feature extraction is a novel method for SAR clustering. The implemented method is based on curvelet subband Gaussian distribution parameter estimation and cascading these estimated values. The implemented method is compared against original data, polarimetric decomposition features and speckle noise reduced data with use of k-means, fuzzy c-means, spatial fuzzy c-means and self-organizing maps clustering methods. Experimental results show that the curvelet subband Gaussian distribution parameter estimation method with use of self-organizing maps has the best results among other feature extraction-clustering performances, with up to 94.94% overall clustering accuracies. The results also suggest that the implemented method is robust against speckle noise. View Full-Text
Keywords: clustering; curvelet transform; synthetic aperture radar; self-organizing maps clustering; curvelet transform; synthetic aperture radar; self-organizing maps
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Uslu, E.; Albayrak, S. Synthetic Aperture Radar Image Clustering with Curvelet Subband Gauss Distribution Parameters. Remote Sens. 2014, 6, 5497-5519.

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