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Remote Sens. 2016, 8(6), 498; doi:10.3390/rs8060498

Wake Component Detection in X-Band SAR Images for Ship Heading and Velocity Estimation

1
Department of Industrial Engineering, University of Naples “Federico II”, Piazzale Tecchio, 80, 80125 Naples, Italy
2
Department of Industrial and Information Engineering, Second University of Naples, via Roma, 29, 81031 Aversa, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Xiaofeng Li and Prasad S. Thenkabail
Received: 27 January 2016 / Revised: 25 May 2016 / Accepted: 7 June 2016 / Published: 14 June 2016
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Abstract

A new algorithm for ship wake detection is developed with the aim of ship heading and velocity estimation. It exploits the Radon transform and utilizes merit indexes in the intensity domain to validate the detected linear features as real components of the ship wake. Finally, ship velocity is estimated by state-of-the-art techniques of azimuth shift and Kelvin arm wavelength. The algorithm is applied to 13 X-band SAR images from the TerraSAR-X and COSMO/SkyMed missions with different polarization and incidence angles. Results show that the vast majority of wake features are correctly detected and validated also in critical situations, i.e., when multiple wake appearances or dark areas not related to wake features are imaged. The ship route estimations are validated with truth-at-sea in seven cases. Finally, it is also verified that the algorithm does not detect wakes in the surroundings of 10 ships without wake appearances. View Full-Text
Keywords: wake detection; Radon transform; ship velocity; ship heading wake detection; Radon transform; ship velocity; ship heading
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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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MDPI and ACS Style

Graziano, M.D.; D’Errico, M.; Rufino, G. Wake Component Detection in X-Band SAR Images for Ship Heading and Velocity Estimation. Remote Sens. 2016, 8, 498.

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