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    Abstract
2012 (Vol. 4, Issue: 16)
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 SAR, polarimetric decomposition, ship detection, support vector machine, texture, ,
Vol. 4 , (16): 2844-2850
Submitted Accepted Published
March 31, 2012 April 17, 2012 August 15, 2012
Abstract:

A Support Vector Machine (SVM) based method for ship detection in Polarimetric SAR (POLSAR) is proposed in this study. 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(16): 2844-2850.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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