Research Papers

Hierarchical vessel classifier based on multifeature joint matching for high-resolution inverse synthetic aperture radar images

[+] Author Affiliations
Zhao Hongyu

National University of Defense Technology, School of Electronic Science and Engineering, Changsha 410073, China

Wang Quan

National University of Defense Technology, School of Electronic Science and Engineering, Changsha 410073, China

Wu Weiwei

National University of Defense Technology, School of Electronic Science and Engineering, Changsha 410073, China

Wang Qingping

National University of Defense Technology, School of Electronic Science and Engineering, Changsha 410073, China

Jiao Shenghai

Beijing Institute of Space Long March Vehicle, Beijing 010, China

Yuan Naichang

National University of Defense Technology, School of Electronic Science and Engineering, Changsha 410073, China

J. Appl. Remote Sens. 8(1), 083563 (Aug 22, 2014). doi:10.1117/1.JRS.8.083563
History: Received April 19, 2014; Revised June 18, 2014; Accepted July 9, 2014
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Abstract.  Vessel classification using inverse synthetic aperture radar (ISAR) imagery is important because it can be used for maritime surveillance and has a high military value. We propose a vessel classification algorithm based on multifeature joint matching. We first utilize a preprocessing method to eliminate the vessel wakes and strong sea clutter, which interfere with feature extraction. In view of the different categories of vessels, we then propose a new two-dimensional strong scattering points encoding (SSPE2-D) for vessel recognition. Furthermore, we modify the method to calculate the number of peaks in the range profile in order to obtain a more accurate result. The high-resolution ISAR images obtained as a result are used to verify the effectiveness of our method. We also compare our proposed method with three other classification methods, and show that the classification rate obtained using our technique is more accurate than that from each of the other methods. Our experiments also show that the preprocessing and the new encoding feature improve classification accuracy.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Zhao Hongyu ; Wang Quan ; Wu Weiwei ; Wang Qingping ; Jiao Shenghai, et al.
"Hierarchical vessel classifier based on multifeature joint matching for high-resolution inverse synthetic aperture radar images", J. Appl. Remote Sens. 8(1), 083563 (Aug 22, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083563


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