Wave Height Estimation From X-Band Nautical Radar Images Using Temporal Convolutional Network
Weimin Huang, Zhiding Yang, Xinwei Chen
Abstract
In this paper, a temporal convolutional network (TCN)-based model is proposed to retrieve significant wave height (Hs) from X-band nautical radar images. Three types of features are first extracted from radar image sequences based on signal to noise ratio (SNR), ensemble empirical mode decomposition (EEMD), and gray level co-occurrence matrix (GLCM) methods, respectively. Then, feature vectors are input into the proposed TCN-based regression model to produce Hs estimation. Radar data collected from a moving vessel at the East Coast of Canada, as well as the simultaneous wave data measured by several wave buoys deployed nearby are used for model training and testing. Experimental results after averaging show that TCN-based model further improves the Hs estimation accuracy, with reductions of root-mean-square errors (RMSEs) by 0.33 m and 0.10 m, respectively, compared to the SNR-based and the EEMD-based linear fitting methods. It has also been found that under the same feature extraction scheme, TCN outperforms other machine learning-based algorithms including support vector regression (SVR) and the gated recurrent unit (GRU) network.