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Abstract
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Article Information:
Ship Detection in Polarimetric SAR Based on Support Vector Machine
Xuwu Su, Guangyou Yang and Hongshi Sang
Corresponding Author: Xuwu Su
Submitted: April 17, 2012
Accepted: May 18, 2012
Published: September 15, 2012 |
Abstract:
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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%.
Key words: Polarimetric decomposition, polarimetric sar, ship detection, support vector machine, texture, ,
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Abstract
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Cite this Reference:
Xuwu Su, Guangyou Yang and Hongshi Sang, . Ship Detection in Polarimetric SAR Based on Support Vector Machine. Research Journal of Applied Sciences, Engineering and Technology, (18): 3448-3454.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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