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Remote Sens. 2017, 9(2), 145; doi:10.3390/rs9020145

An Improved Shape Contexts Based Ship Classification in SAR Images

1
Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, 100190 Beijing, China
2
University of the Chinese Academy of Sciences, Beijing 100039, China
*
Author to whom correspondence should be addressed.
Received: 8 December 2016 / Accepted: 4 February 2017 / Published: 10 February 2017
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
View Full-Text   |   Download PDF [3508 KB, uploaded 13 February 2017]   |  

Abstract

In synthetic aperture radar (SAR) imagery, relating to maritime surveillance studies, the ship has always been the main focus of study. In this letter, a method of ship classification in SAR images is proposed to enhance classification accuracy. In the proposed method, to fully exploit the distinguishing characters of the ship targets, both topology and intensity of the scattering points of the ship are considered. The results of testing the proposed method on a data set of three types of ships, collected via a space-borne SAR sensor designed by the Institute of Electronics, Chinese Academy of Sciences (IECAS), establish that the proposed method is superior to several existing methods, including the original shape contexts method, traditional invariant moments and the recent approach. View Full-Text
Keywords: ship classification; improved shape contexts; scattering centers; synthetic aperture radar (SAR) image ship classification; improved shape contexts; scattering centers; synthetic aperture radar (SAR) image
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhu, J.-W.; Qiu, X.-L.; Pan, Z.-X.; Zhang, Y.-T.; Lei, B. An Improved Shape Contexts Based Ship Classification in SAR Images. Remote Sens. 2017, 9, 145.

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