Wind Speed Estimation From X-Band Marine Radar Images Using Support Vector Regression Method

A support vector regression (SVR)-based method for estimating wind speed from X-band marine radar images is proposed. The dependence of histogram pattern of radar images on wind speed and rain condition is first observed. Then, the feature vectors based on bin values of histograms are extracted and trained using an SVR algorithm. Radar images and anemometer data collected from several periods in a sea trial of the east coast of Canada are used for model training and testing. Experimental results show that compared with the ensemble empirical mode decomposition-based methods, the accuracy of wind speed estimation is improved with a reduction of about 0.14 m/s for rain-free images and 0.11 m/s for rain-contaminated images in root mean square error. Moreover, the proposed method also shows high efficiency by greatly reducing the computational time.