## 4 Experimental Results and Analysis
### Ship Classification Considering Geometric Features  
This section only adopts the geometric features \(f_{\text{g}}\) of ships as the input of the SVM and random forest. The classification accuracy of the trained SVM and random forest model on the test dataset is presented in Table 5, and the maximum classification accuracy of the models is marked in bold. It can be observed that when the geometric features are used, the highest classification accuracy of the test dataset is 73.10%. The classification \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Motion Features**} & \multirow{2}{*}{**Evaluating Indicators**} & \multicolumn{6}{c}{**Ship Types**} \\ \cline{3-8}  & & **Cargo** & **Tanker** & **Fishing** & **Passenger** & **Tug** \\ \hline \multirow{2}{*}{_Longitude\_Span (\({}^{\circ}\))_} & Mean value & 200.21 & 166.74 & 86.49 & 69.56 & 22.16 \\  & Std. deviation & 135.13 & 130.17 & 126.8 & 120.54 & 58.08 \\ \hline \multirow{2}{*}{_Latitude\_Span (\({}^{\circ}\))_} & Mean value & 60.01 & 53.95 & 21.16 & 29.99 & 10.42 \\  & Std. deviation & 27.93 & 26.51 & 24.07 & 43.91 & 18.07 \\ \hline \multirow{2}{*}{_Voyage\_Distance (km)_} & Mean value & 65,373.68 & 54,165.88 & 33,637.14 & 33,934.47 & 9440.48 \\  & Std. deviation & 39,046.90 & 71,069.34 & 144,369.14 & 45,860.41 & 20,906.18 \\ \hline \multirow{2}{*}{_High\_Speed\_Mean (knot)_} & Mean value & 12.42 & 12.35 & 10.49 & 13.49 & 9.02 \\  & Std. deviation & 3.42 & 3.18 & 10.49 & 8.64 & 8.52 \\ \hline \multirow{2}{*}{_High\_Speed\_Std (knot)_} & Mean value & 1.93 & 1.69 & 2.77 & 2.88 & 2.55 \\  & Std. deviation & 2.10 & 1.74 & 5.64 & 3.95 & 5.65 \\ \hline \multirow{2}{*}{_Low\_Speed\_Mean (knot)_} & Mean value & 1.53 & 1.52 & 2.04 & 1.21 & 1.37 \\  & Std. deviation & 0.95 & 0.91 & 1.02 & 0.80 & 0.89 \\ \hline \multirow{2}{*}{_Low\_Speed\_Std (knot)_} & Mean value & 1.15 & 1.07 & 1.07 & 1.13 & 1.19 \\  & Std. deviation & 0.52 & 0.51 & 0.37 & 0.48 & 0.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The statistical results of ship behavior characteristics. accuracy of the random forest is better than that of the SVM, and the random forest also shows its advantages in other evaluation metrics. In addition, compared with fishing ships, passenger ships, and tug ships, the _F\({}_{1}\)-Score_ of cargo ships and tanker ships is relatively high. However, the overall performance of both the classifiers is poor, which could be improved by using more ship features. Figure 4 shows the classification confusion matrixes of the SVM and random forest on the test dataset. The \"true label\" is the real label of ships in the AIS data, and the \"predicted label\" is the classification result of the classifiers. It can be seen that there is classification confusion in the prediction results of both the classifiers. The number of misclassification samples of the random forest is less than that of the SVM. It is worth noting that cargo ships and tanker ships are easily confused in the process of classification, which can be explained by the fact that these two types of ships have certain similarities in shape design and construction. Fishing ships, passenger ships, and tug ships also have classification confusion, which is more serious than that of cargo ships and tanker ships. When the geometric features are considered, both the classifiers are prone to misclassifying ship samples. In order to improve the classification confusion, it is necessary to adopt more distinguishing ship features. Figure 4: _Cont._