A Novel Scheme for Extracting Sea Surface Wind Information from Rain-contaminated X-band Marine Radar Images

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 14; p. 1
• Main Authors: Chen, Xinwei, Huang, Weimin, Haller, Merrick
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Atmospheric precipitations; Clustering; Contamination; Curve fitting; Estimation; Feature extraction; Haze; image dehaz-ing; Image dehazing; Marine pollution; Measurement; Neural networks; Parameter estimation; Performance degradation; Radar; Radar data; Radar imaging; Radar measurements; Radar remote sensing; Rain; Regression analysis; Regression models; Sea surface; Self organizing maps; Superhigh frequencies; Support vector machines; Surface wind; Training; wind; Wind direction; Wind measurement; Wind speed; X-band marine radar
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2021.3078902

The presence of rain degrades the performance of sea surface parameter estimation using X-band marine radar. In this paper, a novel scheme is proposed to improve wind measurement accuracy from rain-contaminated X-band marine radar data. After extracting texture features from each image pixel, the rain-contaminated regions with blurry wave signatures are first identified using a self-organizing-map (SOM)-based clustering model. Then, a convolutional neural network (CNN) used for image haze removal, i.e., DehazeNet is introduced and incorporated into the proposed scheme for correcting the influence of rain on radar images. In order to obtain wind direction information, curve fitting is conducted on the average azimuthal intensities of rain-corrected radar images. On the other hand, wind speed is estimated from rain-corrected images by training a support-vector-regression (SVR)-based model. Experiments conducted using datasets from both shipborne and onshore marine radar show that compared to results obtained from images without rain correction, the proposed method achieves relatively high estimation accuracy by reducing measurement errors significantly.