Evaluation and Mitigation of Rain Effect on Wave Direction and Period Estimation From X-Band Marine Radar Images

Yang, Zhiding and Huang, Weimin and Chen, Xinwei (2021) Evaluation and Mitigation of Rain Effect on Wave Direction and Period Estimation From X-Band Marine Radar Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14. pp. 5207-5219. ISSN 2151-1535

[img] [English] PDF - Published Version
Available under License Creative Commons Attribution Non-commercial.

Download (7MB)


In this article, the accuracy of wave direction and period estimation from X-band marine radar images under different rain rates is analyzed, and a simple subimage selection scheme is proposed to mitigate the rain effect. First, each radar image is divided into multiple subimages, and the subimages with relatively clear wave signatures are identified based on the random-forest-based classification model. Then, wave direction is estimated by performing the Radon transform on each valid subimage. As for wave period estimation, a new method is proposed. Texture features are first extracted from each pixel of the selected subimage using the gray-level co-occurrence matrix and combined as a feature vector. Those feature vectors extracted from both rain-free and rain-contaminated training samples are then used to train a random-forest-based wave period regression model. The shore-based X-band marine radar images, simultaneous rain rate data, as well as buoy-measured wave data collected on the West Coast of the United States are used to analyze the rain effect on wave parameter estimation accuracy and validate the proposed method. Experimental results show that the proposed subimage selection scheme improves the estimation accuracy of both wave direction and wave period under different rain rates, with reductions of root-mean-square errors (RMSEs) by 6.9 ° , 6.0 ° , 4.9 ° , and 1.0 ° for wave direction under rainless, light rain, moderate rain, and heavy rain conditions, respectively. As for wave period estimation, the RMSEs decrease by 0.13, 0.20, 0.30, and 0.20 s under those four rainfall intensity levels, respectively.

Item Type: Article
URI: http://research.library.mun.ca/id/eprint/15363
Item ID: 15363
Additional Information: Memorial University Open Access Author's Fund
Keywords: Rain, random forest, subimage selection, wave, X-band marine radar
Department(s): Engineering and Applied Science, Faculty of
Date: 29 April 2021
Date Type: Publication
Digital Object Identifier (DOI): https://doi.org/10.1109/JSTARS.2021.3076693
Related URLs:

Actions (login required)

View Item View Item


Downloads per month over the past year

View more statistics