Abstract: Due to the limited vision and cost of the monitoring equipment,the classification methods of ships based on images or videos are not very effective.So it is urgent to improve classification methods of the ships and the accuracy of those methods.In recent years,with the widelyused of various trajectory data acquisition systems,it has become possible to classify ship types through ship trajectory data.Based on the problem that the traditional two-dimensional convolutional neural network is lacking the ability of feature compression and temporal feature expression in ship trajectory recognition,this paper proposes a hybrid model which combines one-dimensional convolutional neural network (IDCNN) with long short-term memory (LSTM).This model can identify ship types by using the data collected from the automatic identification system (AIS).Firstly,this paper preprocesses the ship trajectory data collected by AIS to filter the noise data.Secondly,to solve the problem that the features hidden in the original ship trajectory information are over obscurity for 1DCNN,this paper proposes an algorithm for constructing the trajectory distribution feature vectors which can be accepted by 1DCNN for a large number of ship trajectory data.On this basis,the algorithm extracts the time series feature vectors which can be accepted by LSTM.Finally,this paper combines the trained 1DCNN model and LSTM model to get a hybrid ship classification model.Based on the AIS data of Bohai area on June 2016, the hybrid model combining 1DCNN with LSTM is used to classify five different typical ships including fishing ships,passenger ships,tanker ships,container ships and bulk-cargo ships.The experimental results show that compared with the method of using a neural network such as LSTM as classifier,the proposed method is obviously effective,and is an effective ship trajectory classification method.

Key words: Ship trajectory classification, One-dimensional convolutional neural network(1DCNN), Long short-term memory(LSTM), Distribution feature vector, Time series feature vector