# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 5 Conclusions  
In this paper, the authors proposed the use of a multi-feature fusion with a CNN method for ship classification. To facilitate the training and performance evaluation of the proposed multi-feature fusion CNN framework, the authors used the VAIS dataset to test the proposed method. Simultaneously, they established their own ship dataset comprising a combination of ship images captured along the Yangtze River channel and those obtained from ship-image databases of the China Shipping Service and Baidu websites. Compared to fine-VGG-16, fine-ResNet-18, and other deep CNNs, the proposed improved CNN was characterized by shallow layers and relatively few parameters, thereby reducing its computational complexity. HOG features are used to extract edge features and LBP features are used to extract texture features. These two handcrafted features were adopted to compensate for the shortcomings of CNN (that is, partial local features are lost) to more accurately describe ship images. In addition, the advantages of these three types of features were considered, and they were fused to obtain a robust ship classification model. Results of experiments performed in this study demonstrated that the average classification accuracy of the proposed method was equal to 97.50% and 93.60%, respectively, when applied to the limited number of self-built and VAIS datasets, respectively. Additionally, a consideration of evaluation metrics, such as the F1-score, classification accuracy of each class, confusion matrix, and average time consumption of the feature extraction per image, revealed that classification performance of the proposed method was superior to other state-of-the-art methods. This also implies that the proposed method performed better at extracting features from ship images. Compared with other deep networks, the improved CNN also had better classification ability. However, results obtained concerning the VAIS dataset demonstrated that there still existed room for improvement in classification performance of the proposed method. As a future endeavor, the authors intend to enhance further the classification ability of the proposed method from the viewpoints of the extended dataset and transfer learning.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Average Time Consumption of Feature Extraction (ms)** \\ \hline HOG + SVM & 4.265 \\ LBP + SVM & 1.409 \\ CNN [38] & 0.241 \\ Fine-VGG-16 & 0.756 \\ Fine-ResNet-18 & 0.913 \\ HOG + CNN & 8.784 \\ LBP + CNN & 8.548 \\ Improved CNN & 0.104 \\ Proposed Method & 15.329 \\ \hline \hline \end{tabular}
\end{table}
Table 14: The average time consumption of the feature extraction per image for the VAIS dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Classified/Real** &
\begin{tabular}{c} **Average Time Consumption of** \\ **the Feature Extraction Per Image (ms)** \\ \end{tabular} \\ \hline Proposed Method & 98.33 & 589/599 & 16.341 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Experiment with the original images test dataset.  
**Author Contributions:** [PERSON] conceived the manuscript and conducted the whole experiments. [PERSON] supervised the experiments and helped discuss the proposed the method. [PERSON] and [PERSON] contributed to the organization of the paper and gave helpful suggestion on this research. All authors read and approved the final manuscript.  
**Funding:** This work was supported by the National Science Foundation of China (Grant No. 51879211), and the Scientific Research Project of the Hunan Provincial Education Department (Grant No. 18C0900).  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their very competent comments and helpful suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.