###### Abstract
These methods can overcome the problems wherein the previous statistical analysis of AIS data required relevant professional knowledge background and precise assumptions to a certain extent, greatly promoting the intellectualization of maritime traffic management and ship navigation based on AIS data and further deepening the understanding of maritime ship behavior. Nonetheless, the application of AIS data in maritime surveillance still faces many difficulties. In recent years, the development of AIS has promoted research on ship behavior patterns, and the description of maritime traffic characteristics has been more detailed than in previous studies. AIS data have been successfully applied to ship motion analysis, ship collision risk assessment, and ship classification and anomaly detection, which has effectively promoted the development of maritime surveillance technology. However, some ships with AIS evade detection and perform illegal operations or even illegal exploration by turning off the transponder, forging position data, and deliberately transmitting false identity information [21]. In addition, AIS data also have problems such as mislabeling of ship type information, data imbalance, and poor data quality [22; 23]. These problems hinder the further mining of AIS data and pose many challenges to maritime surveillance. As the basis of AIS data mining, ship classification can be used to determine the types of ships that play a crucial role in promoting the application of maritime surveillance. At present, some existing research works have achieved a certain effect by extracting ship features and combining them with machine learning methods (e.g., SVM, KNN, and CNN) for ship classification. However, most of these studies have some disadvantages, such as the few ship types classified, incomplete ship feature extraction, and short time and small range of AIS data used. In addition, the performance of ship classification methods has yet to be improved. If more global dynamic information of AIS data can be considered, it will be beneficial to obtain more comprehensive ship behavior characteristics for ship monitoring. This paper proposes a ship classification and anomaly detection method based on spaceborne AIS data for ships worldwide by considering ship behavior characteristics. Based on the AIS data collected by ocean series satellites, this paper makes full use of the advantages of spaceborne AIS data, such as wide coverage, long tracking time, and rich ship types, and combines machine learning methods to conduct AIS data mining to improve the monitoring ability of ships in the open sea. This paper focuses on spaceborne AIS data mining in maritime surveillance, namely ship classification and anomaly detection. The major contributions of this paper are the following:  
1. In the process of ship classification and identification, we carried out a comprehensive feature extraction project on the global AIS data to form 13-dimensional features, including geometric features and behavior characteristics (course distance and sailing speed were considered), which enriches the input of classification models. 2. We conducted research on the ship classification of spaceborne AIS data based on machine learning algorithms. The influence of different classifiers and feature combinations on the classification performance was analyzed and discussed. Experiments showed that the performance of classifiers can be improved by using the extracted behavior characteristics. 3. Case studies of ship anomaly detection were presented and analyzed, and the experimental results demonstrate that the proposed method can effectively solve the ship anomaly detection problem in maritime surveillance. The remainder of this paper is organized as follows: Section 2 reviews ship classification methods based on AIS data. Section 3 introduces the implementation of the proposed method in detail. This section first analyzes the spaceborne AIS data, including data preprocessing and ship type analysis.