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Vessel monitoring is one of the most important maritime applications of Synthetic Aperture Radar (SAR) data. Because of the dihedral reflections between the vessel hull and sea surface and the trihedral reflections among superstructures, vessels usually have strong backscattering in SAR images. Furthermore, in high-resolution SAR images, detailed information on vessel structures can be observed, allowing for vessel classification in  high-resolution SAR images. This paper focuses on the feature analysis of merchant vessels, including bulk carriers, container ships and oil tankers, in 3 m resolution COSMO-SkyMed stripmap HIMAGE mode images and proposes a method for vessel classification. After preprocessing, a feature vector is estimated by calculating the average value of the kernel density estimation, three structural features and the mean backscattering coefficient. Support vector machine (SVM) classifier is used for the vessel classification, and the results are compared with traditional methods, such as the K-nearest neighbor algorithm (K-NN) and minimum distance classifier (MDC). In situ investigations are conducted during the SAR data acquisition. Corresponding Automatic Identification System (AIS) reports are also obtained as ground truth to evaluate the effectiveness of the classifier. The preliminary results show that the combination of the average value of the kernel density estimation and mean backscattering coefficient has good ability for classifying the three types of vessels. When adding the three structural features, the results slightly improve. The result of the SVM classifier is better than that of K-NN and MDC. However, the SVM requires more time, when the parameters of the kernel are estimated

Classification of Vessels in Single-Pol COSMO-SkyMed Images Based on Statistical and Structural Features