The presence of rain may blur surface wave signatures and cause additional radar backscatter, which negatively affects the performance of ocean remote sensing applications (e.g., ocean surface wind and wave parameter measurement) using X-band marine radars. In this article, a novel end-to-end model is developed to detect and locate rain-contaminated pixels in X-band marine radar images based on a type of deep neural network called SegNet, which is able to segment rain-contaminated regions by classifying each pixel into three classes: rain-free, rain-contaminated, and wind-dominated rain cases. Shipborne marine radar images collected during a sea trial on the East Coast of Canada are first preprocessed and then utilized to train an ensemble of SegNet-based networks. The final classification result of each pixel will be the class chosen by most individual networks. Testing results using images obtained from both shipborne and shore-based marine radar systems manifest that the proposed model effectively segment between rain-free, rain-contaminated, and wind-dominated rain regions, with a pixel classification accuracy of 94.6% and 90.4% for Decca and Koden radar images, respectively.

中文翻译：

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使用 SegNet 集合对 X 波段海洋雷达图像进行雨水污染区域分割

降雨的存在可能会模糊表面波特征并导致额外的雷达反向散射，这会对使用 X 波段海洋雷达的海洋遥感应用（例如，海洋表面风和波浪参数测量）的性能产生负面影响。在本文中，基于一种称为 SegNet 的深度神经网络，开发了一种新颖的端到端模型来检测和定位 X 波段海洋雷达图像中的雨水污染像素，该网络能够通过以下方式分割雨水污染区域将每个像素分为三类：无雨、雨污染和风主导的雨情况。在加拿大东海岸进行海上试验期间收集的舰载海洋雷达图像首先经过预处理，然后用于训练基于 SegNet 的网络集合。每个像素的最终分类结果将是大多数单个网络选择的类。使用从船载和岸基海洋雷达系统获得的图像进行的测试结果表明，所提出的模型有效地分割了无雨、受雨污染和以风为主的雨区，像素分类精度分别为 94.6% 和 90.4%。分别是 Decca 和 Koden 雷达图像。