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Remote Sens. 2017, 9(2), 166;

Texture-Analysis-Incorporated Wind Parameters Extraction from Rain-Contaminated X-Band Nautical Radar Images

Department of Electrical and Computer Engineering, Memorial University, St. John’s, NL A1B 3X9, Canada
Author to whom correspondence should be addressed.
Academic Editors: Francesco Soldovieri, Raffaele Persico, Xiaofeng Li and Prasad S. Thenkabail
Received: 18 November 2016 / Revised: 10 February 2017 / Accepted: 14 February 2017 / Published: 16 February 2017
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
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In this paper, a method for extracting wind parameters from rain-contaminated X-band nautical radar images is presented. The texture of the radar image is first generated based on spatial variability analysis. Through this process, the rain clutter in an image can be removed while the wave echoes are retained. The number of rain-contaminated pixels in each azimuthal direction of the texture is estimated, and this is used to determine the azimuthal directions in which the rain-contamination is negligible. Then, the original image data in these directions are selected for wind direction and speed retrieval using the modified intensity-level-selection-based wind algorithm. The proposed method is applied to shipborne radar data collected from the east Coast of Canada. The comparison of the radar results with anemometer data shows that the standard deviations of wind direction and speed using the rain mitigation technique can be reduced by about 14.5° and 1.3 m/s, respectively. View Full-Text
Keywords: wind field; rain; texture; X-band nautical radar wind field; rain; texture; X-band nautical radar

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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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Huang, W.; Liu, Y.; Gill, E.W. Texture-Analysis-Incorporated Wind Parameters Extraction from Rain-Contaminated X-Band Nautical Radar Images. Remote Sens. 2017, 9, 166.

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