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

Hongyu Zhao; Quan Wang; Weiwei Wu; Qingping Wang; Jiao Shenghai; Naichang Yuan

doi:10.1117/1.JRS.8.083563

22 August 2014 Hierarchical vessel classifier based on multifeature joint matching for high-resolution inverse synthetic aperture radar images

Hongyu Zhao, Quan Wang, Weiwei Wu, Qingping Wang, Jiao Shenghai, Naichang Yuan

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 8, Issue 1, 083563 (August 2014). https://doi.org/10.1117/1.JRS.8.083563

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 (SSPE _2-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.

Citation Download Citation

Hongyu Zhao, Quan Wang, Weiwei Wu, Qingping Wang, Jiao Shenghai, and Naichang Yuan "Hierarchical vessel classifier based on multifeature joint matching for high-resolution inverse synthetic aperture radar images," Journal of Applied Remote Sensing 8(1), 083563 (22 August 2014). https://doi.org/10.1117/1.JRS.8.083563

Published: 22 August 2014

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