###### Abstract
[15] extract the statistical features from each temporal variable of AIS data, and the features are then input into a Gradient Boosting classifier; their method has an accuracy of 0.86. [PERSON] et al. [16] use p-GRUs (Partition-wise Gated Recurrent Units) to achieve the recognition of trawlers with an accuracy of 0.89. Another similar approach is presented in [17], which uses RNN (recurrent neural network) to classify five types of ships, with an accuracy of 0.783. In addition, some methods combine dynamic and static features. [PERSON] et al. [18] extract geographical distribution features, motion features, time of start/stop, and the static shape features of vessels from AIS data from German  
Figure 1: Ships’ distribution with type unknown in message1. Bight and achieve an accuracy of 0.9751 on a five-classification task--however, the method has a data leakage problem. [PERSON] et al. (2019) integrate the vessel's course change, speed, and environmental information (i.e., tide, light, and water temperature) to identify six types of fishing vessel activities in the waters around Jeju Island, achieving an accuracy of 0.963. However, there still exists some disadvantages in the methods mentioned above, and they are summarized as follows. * In some studies, only dynamic or static features are used for ship classification. As such, the utilization of multiple characteristics of ships is lacking, and the dynamic features are mainly set manually and empirically. * Most of the existing studies use shore-based data which is usually distributed in a small area, for which the ship trajectories and motion features are restricted. For example, in inland rivers or ports, the ships' position, speed, and direction are subject to limitations associated with the navigation channels, leading to an insufficient generalization ability of the classifier. Moreover, there is a lack of methods applicable to worldwide ship classification. * The characteristics of space-based AIS data are different from those of shore-based data. Due to the limited number of satellites and the AIS signal conflict, global real-time coverage of AIS cannot currently be achieved. The continuity of space-based AIS data is weak, and there are few long-term ship trajectories with high continuity. The existing ship classification method may not be suitable for space-based AIS data. * The classification number of ships in some researches is few, the differences between ships of different types are obvious, and the binary classification methods have limited application value. * When splitting the sub-trajectories set, some researchers do not specifically distinguish the sources of sub-trajectories, which causes data from the same ship to appear in the training sets, validation sets, and testing sets. This data leakage problem will lead to the performance of the classifiers being overestimated. To solve the problems outlined above, this paper proposes a multi-feature ensemble learning classification method (MFELCM) that integrates ships' static and dynamic information. The method applies to the global range of satellite-based AIS data. The detailed process of MFELCM is shown in Figure 2, which consists of three steps. In the first step, the original data are preprocessed, and the cleaned static and dynamic data are then converted into static feature samples, dynamic feature distribution samples, time-series samples, and time-series feature samples. In the second step, four base classifiers, namely Random Forest (20), 1D-CNN (one-dimensional convolutional neural network) ([PERSON] et al., 2015), Bi-GRU, and XGBoost (extreme gradient boosting) ([PERSON] et al., 2015), are trained by the samples above. In the third step, another Random Forest is applied in order to integrate the base classifiers as MFELCM. The main contributions of this paper are as follows. * A multiple-perspectives method of ship feature description is proposed in order to extract the dynamic and static features of ships from space-based AIS data.