# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 4 Experimental Results and Analysis
### Classification Results and Analysis
#### 4.4.2 Comparison of the F1-Score and Confusion Matrix  
To further validate the performance of the improved method, values concerning the F1-score obtained using these different CNN methods are listed in Tables 11 and 12. As observed, compared to other techniques, the proposed method demonstrated attainment of the highest average F1-score along with high classification accuracy for each class. For the self-build dataset, the CNN method presented in extant studies [29] demonstrates poor classification accuracy for bulk ships owing to the similarity between some bulk ships and container ships, which makes it difficult to distinguish them accurately. The fine-AlexNet, CNN [38], and fine-VGG-16 methods demonstrated the better classification performance for sailing ships. For the VAIS dataset, the average values of F1-scores corresponding to the proposed method were the highest. While the other methods demonstrated better classification performance with regard to sailing ships, their performance with regard to the classification of the medium-other and medium-passenger ship types was rather poor. Since the appearance of the medium-other and medium-passenger ship types was more complex and that there existed considerable difference between sailing and other ship classes, it was much easier to distinguish sailing ships. Compared with other methods, the proposed method had an improved F1-score for each category. On the one hand, this indicates that fine-VGG-16 and fine-ResNet-18 caused overfitting owing to the small ship dataset, and they were better suited to deal with complex classification task. On the other hand, it demonstrated that the proposed method could better extract ship features and that it offered superior ship classification performance after combining CNN high-level features with handcrafted features of HOG and LBP. Figure 15 depicts the confusion matrix and confusion matrix normalization corresponding to the proposed method for the self-built dataset. As can be seen, diagonal elements of the confusion matrix and its normalized form denoted the correct quantity of each ship class and classification accuracy achieved per class, respectively. It was easily found that major confusion occurred between class 0 (i.e., bulk ships) and class 1 (i.e., container), or between class 1 and class 3 (i.e., sailing). It was observed that the length of bulk ships was similar to that of container carriers, some images of bulk were similar to these of the container. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Class**} \\ \cline{2-7}  & **Medium-Other** & **Merchant** & **Medium-Passenger** & **Sailing** & **Small** & **Tug** \\ \hline HOG + SVM & 70.93 & 85.92 & 75.81 & 93.94 & 92.33 & 75.00 \\ LBP + SVM & 55.81 & 76.06 & 67.74 & 81.82 & 88.82 & 35.00 \\ HOG + CNN & 76.74 & 87.32 & 83.87 & 95.96 & 92.33 & 95.00 \\ LBP + CNN & 77.91 & 87.32 & 87.10 & 94.95 & 93.61 & 95.00 \\ Improved CNN & 80.23 & 87.32 & 88.71 & 94.95 & 94.89 & 100.00 \\ Proposed Method & 82.56 & 88.73 & 90.32 & 97.47 & 95.53 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Class-specific accuracy (%) of different methods for the VAIS dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Class**} \\ \cline{2-5}  & **Bulk** & **Container** & **Passenger** & **Sailing** \\ \hline HOG + SVM & 83.53 & 82.52 & 81.62 & 93.33 \\ LBP + SVM & 69.08 & 79.50 & 70.34 & 86.22 \\ HOG + CNN & 93.64 & 91.43 & 92.16 & 94.89 \\ LBP + CNN & 89.88 & 93.28 & 91.91 & 95.78 \\ Improved CNN & 95.95 & 95.97 & 96.56 & 97.56 \\ Proposed Method & 96.82 & 96.30 & 97.79 & 99.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Class-specific accuracy (%) of different methods for the self-built dataset. Figure 16 depicts the confusion matrix and confusion matrix normalization corresponding to the proposed method for the VAIS dataset.