# Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
## 1 Introduction
Using shipborne radar to monitor an oil spill is physically possible because an oil slick on the sea surface has a different viscosity to that of water, and therefore attenuates the short gravity and capillary waves [25]. The roughness of the sea surface becomes less than that of water in an oil spill, and therefore, a lower radar backscattering of oil slicks is formed [26; 27]. This results in oil slicks being displayed as relatively dark targets in the wave area on shipborne radar images. This differs from spaceborne and airborne radar, whereby the gray distribution of the wave in the original shipborne radar image is very uneven, and with increasing monitoring distance, the gray level continuously decreases because they are relatively dark targets. In short distance waves, the gray value of the oil spill may, therefore, be higher than that of the long distance waves. This creates significant difficulty for shipborne oil spill monitoring. Many research methods for spaceborne and airborne radar can be applied in the oil spill detection of shipborne radar images, including manual single threshold segmentation [28], the adaptive threshold segmentation method [29], the double threshold segmentation method [30], the active contour model (ACM) [31], neural networks, and machine learning algorithms [32; 33]. The oil spill monitoring technology of shipborne radar remains in its infancy, and some related research findings have been published. [PERSON] et al. suggested that, after adjusting the entire gray level of a shipborne radar image, the manual single threshold segmentation should be used to extract oil spill [34], hence making exploratory progress. Based on manual single threshold segmentation, [PERSON] et al. proposed a manual dual-threshold segmentation method for shipborne radar image oil spill monitoring after determining the range of effective wave information [35]. With the continuous development of oil spill detection technology in shipborne radar images, [PERSON] et al. suggested an adaptive threshold method to automatically identify oil films [36]. In recent years, deep learning methods have been successfully applied in the pattern recognition of images. [PERSON] et al. proposed the extraction of oil filmsby a local adaptive threshold after determining the surrounding oil spill areas using a deep learning method with textural features of a Gray Level Co-occurrence Matrix (GLCM) [37]. In view of the successful application of deep learning on shipborne radar oil spill monitoring, we propose extracting effective wave information from radar images based on a support vector machine (SVM), and then extracting oil films based on a local adaptive threshold. In this study, a complete oil spill detection method of shipborne radar images is advocated. A large number of co-frequency interferences seriously affect the gray distribution of the original image, and we therefore offer a fast noise reduction method in a Cartesian coordinate system, with the SVM used to classify the waves and backgrounds. According to the gray distribution matrix, the effective wave monitoring zone is then chosen to segment oil films by a local adaptive threshold. The final result is mapped to the electronic chart through coordinate transformation. The successful application of this scheme will be an effective technical means for the real-time monitoring of oil spills. The remainder of this paper is organized as follows: Section 2 focuses on the preparation process of the materials and methods, including the description, preprocessing, and major methods. Section 3 presents the results of our experiments and the validity of our method. The advantages of shipborne radar oil spill monitoring in comparison with other local adaptive thresholds as well as in comparison with other oil film classifications of shipborne radar are also provided. Section 4 provides a summary of the study.