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Sensors 2015, 15(10), 25072-25089; doi:10.3390/s151025072

A Novel Fusion-Based Ship Detection Method from Pol-SAR Images

1
Institute of Electronic and Information Engineering, Beihang University, Beijing 100191, China
2
Lucent Technology Investment Limited, Beijing 100073, China
*
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 4 June 2015 / Revised: 9 August 2015 / Accepted: 23 September 2015 / Published: 29 September 2015
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [2983 KB, uploaded 29 September 2015]   |  

Abstract

A novel fusion-based ship detection method from polarimetric Synthetic Aperture Radar (Pol-SAR) images is proposed in this paper. After feature extraction and constant false alarm rate (CFAR) detection, the detection results of HH channel, diplane scattering by Pauli decomposition and helical factor by Barnes decomposition are fused together. The confirmed targets and potential target pixels can be obtained after the fusion process. Using the difference degree of the target, potential target pixels can be classified. The fusion-based ship detection method works accurately by utilizing three different features comprehensively. The result of applying the technique to measured Airborne Synthetic Radar (AIRSAR) data shows that the novel detection method can achieve better performance in both ship’s detection and ship’s shape preservation compared to the result of K-means clustering method and the Notch Filter method. View Full-Text
Keywords: Pol-SAR; ship detection; target decomposition; difference degree Pol-SAR; ship detection; target decomposition; difference degree
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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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Wang, W.; Ji, Y.; Lin, X. A Novel Fusion-Based Ship Detection Method from Pol-SAR Images. Sensors 2015, 15, 25072-25089.

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