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2012 (Vol. 4, Issue: 18)
Article Information:

Ship Detection in Polarimetric SAR Based on Support Vector Machine

Xuwu Su, Guangyou Yang and Hongshi Sang
Corresponding Author:  Xuwu Su 

Key words:  Polarimetric decomposition, polarimetric sar, ship detection, support vector machine, texture, ,
Vol. 4 , (18): 3448-3454
Submitted Accepted Published
April 17, 2012 May 18, 2012 September 15, 2012

In this study, we propose a Support Vector Machine (SVM) based method for ship detection in polarimetric SAR (POLSAR). Because of similarities of ship and man-made structures on land in scattering mechanisms, land and sea are first segmented by SVM according to polarimetric features and texture features; The SVM-based Recursive Feature Elimination (RFE-SVM) approach is adopted to improve the performance of the segmentation algorithm. Then ship targets are extracted from sea by SVM classifier; Threshold-based rules and SVM-based rules are established for discriminating ship from non-ship target at last. The experiments are carried out on POLSAR data from Radarsat-2. For the available SAR images, the average accuracy of ship detection is over 95%.
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  Cite this Reference:
Xuwu Su, Guangyou Yang and Hongshi Sang, 2012. Ship Detection in Polarimetric SAR Based on Support Vector Machine.  Research Journal of Applied Sciences, Engineering and Technology, 4(18): 3448-3454.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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