Rain Detection From X-Band Marine Radar Images: A Support Vector Machine-Based Approach

• Published in: IEEE transactions on geoscience and remote sensing Vol. 58; no. 3; pp. 2115 - 2123
• Main Authors: Chen, Xinwei, Huang, Weimin, Zhao, Chen, Tian, Yingwei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Atmospheric precipitations; Bins; Decca; Detection; Feature extraction; Histograms; Image classification; Image detection; Model testing; Radar; Radar antennas; Radar data; Radar detection; Radar equipment; Radar imaging; Rain; Rain detection; Sea measurements; Sea trials; Superhigh frequencies; Support vector machines; support vector machines (SVMs); Training; Wind speed; X-band marine radar
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2019.2953143

Since rain alters the histogram pattern of radar images, rain-contaminated radar data can be identified. In this article, a support vector machine (SVM)-based method for rain detection using X-band marine radar images is presented. First, the normalized histogram bin values for each image are extracted and combined into feature vector. Then, SVMs are employed to classify between rain-free and rain-contaminated images. Radar images and simultaneous rain rate data collected from a sea trial in North Atlantic Ocean are utilized for model training and testing. Comparison with the zero pixel percentage (ZPP) threshold method shows that the SVM-based method obtains higher detection accuracy, with 98.4% for the Decca radar data and 99.7% for the Furuno radar. It is also found that as the total number of bins does not significantly affect detection accuracy, the proposed method can be applied to different radar systems directly with a suitable number of bins. In addition, compared to the ZPP threshold method, the SVM-based method proves to be more robust even with limited training samples.