Research Papers

Unsupervised synthetic aperture radar image segmentation with superpixels in independent space based on independent component analysis

[+] Author Affiliations
Jian Ji

Xidian University, School of Computer Science and Technology, Xi’an, Shaanxi Province 710071, China

Xiao-yuan Li

Xidian University, School of Computer Science and Technology, Xi’an, Shaanxi Province 710071, China

J. Appl. Remote Sens. 8(1), 083682 (Feb 03, 2014). doi:10.1117/1.JRS.8.083682
History: Received August 25, 2013; Revised December 5, 2013; Accepted December 17, 2013
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Abstract.  Synthetic aperture radar (SAR) image segmentation is a challenging problem in recent years because of the speckle noise. An unsupervised SAR image segmentation with superpixels by independent component analysis (ICA) is proposed. ICA independent space is proposed to represent SAR images for feature extraction effectively. First, the SAR image is divided into small regions by mean-shift algorithm and then those regions are merged in region adjacent graph and full-connected graph based on the Mining Spanning Tree theory, which balances the speed and quality of segmentation. Finally, experiments on X-band TerraSAR images and comparisons with simple linear iterative clustering and graph-cut illustrate the excellent performance of the new method.

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Citation

Jian Ji and Xiao-yuan Li
"Unsupervised synthetic aperture radar image segmentation with superpixels in independent space based on independent component analysis", J. Appl. Remote Sens. 8(1), 083682 (Feb 03, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083682


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