Sea Surface Wave Height Estimation and Improvement from Rain-Contaminated X-Band Nautical Radar Data.

In this work, an improvement for the quality of X-band radar images affected by rain is proposed, and a support vector regression (SVR)-based method is designed to further obtain the significant wave height (H-s). The first step is to implement the dehazing algorithm on the radar images influenced by rain. Then, SVR is employed to train the H-s regression model including two features, i.e., gray level co-occurrence matrix (GLCM) and signal-to-noise ratio (SNR) features, extracted from rainless data. Finally, H-s can be obtained from the trained model with these two features extracted from the dehazed images. Besides, two classical H-s estimation methods, i.e., ensemble empirical mode decomposition (EEMD)- and SNR-based regression algorithms are utilized for analyzing the effectiveness of the dehazing algorithm. Experiment results confirm that dehazing algorithm can substantially decrease the root-mean-square-error (RMSE) and biases of the estimated H-s and increase the correlation coefficients (CCs) between the results and buoy data. Furthermore, comparisons with the SNR- and EEMD-based regression algorithms incorporating the dehazing algorithm illustrate that RMSEs obtained from the proposed method are further reduced to 0.44 m from 0.49 m and 0.78 m, respectively.