# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 4 Experimental Results and Analysis
### Classification Results and Analysis
#### 4.4.1 Comparison of Classification Accuracy  
Tables 7 and 8 list the classification accuracy and number of misclassification samples for the different methods considered in this study for the self-built dataset and VAIS dataset. As can be observed, the proposed method achieved superior classification performance compared to all other existing methods. When applied to the self-built dataset, the average accuracy of the proposed method exceeded those of the fine-AlexNet and a previously proposed CNN approach [38] by nearly 4.28% and 6.56%, respectively. Additionally, the number of misclassification samples corresponding to the proposed method was the least compared to other existing methods. When applied to the VAIS dataset, the accuracy exceeded that of the multiple feature learning (MFL) (feature-level) + SVM [36], andME-CNN [32] methods by 8.27% and 6.27%, respectively. Therefore, the improved method had obvious advantages, as the improved method fully considers the specific features of ship images and combines the high-level and low-level features that are conducive to ship classification. The improved CNN had good image description ability. In addition, HOG and LBP features could describe the edge and spatial texture features of ship images, respectively. Thus, the fusion of these three types of features could yield more distinguishing features and improve the ship classification accuracy. That said, no particular method demonstrated attainment of exceptionally high classification accuracy when applied to the VAIS dataset. This is because ship images contained within the VAIS dataset were of very low resolution and exhibited glare. Figure 14 illustrates that the quality of images was not good, and this played an important role in regards to the performance of the classification method. This further strengthens the argument that VAIS was a very challenging dataset to work with. In addition, to further verify the discrimination ability of the improved method, we compared the classification accuracy obtained per ship class of different methods. The experiment results are listed in Tables 9 and 10. As observed, compared to existing approaches, the proposed method demonstrated better classification performance compared to all other methods. For the self-built dataset, the proposed method had the highest classification accuracy for sailing, LBP + CNN and improved CNN methods also obtained high accuracy for sailing. Due to sailing's distinct shape and size characteristics, it was easy to classify. For the VAIS dataset, the proposed method had the highest classification accuracy for each type, although the number of tug-ship samples in VAIS was quite small. The classification \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Number of Misclassification** \\ \hline HOG + SVM & 84.99 & 270 \\ LBP + SVM & 77.10 & 412 \\ CNN [29] & 87.77 & 220 \\ Fine-AlexNet & 93.22 & 122 \\ Fine-ResNet-18 & 91.50 & 153 \\ Fine-VGG-16 & 92.61 & 133 \\ CNN [38] & 90.94 & 163 \\ HOG + CNN & 92.88 & 128 \\ LBP + CNN & 92.94 & 127 \\ Improved CNN & 96.50 & 63 \\ Proposed Method & 97.50 & 45 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification accuracy and the number of misclassification samples associated with different methods when applied to the self-built dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Accuracy (\%)** & **Number of Misclassification** \\ \hline HOG + SVM & 86.93 & 98 \\ LBP + SVM & 78.80 & 159 \\ MFL (feature-level) + SVM [36] & 85.33 & 110 \\ CNN [19] & 85.75 & 107 \\ CNN [27] & 81.90 & 136 \\ CNN [38] & 86.00 & 105 \\ ME-CNN [32] & 87.33 & 95 \\ Fine-VGG-16 & 86.40 & 102 \\ Fine-ResNet-18 & 90.40 & 72 \\ HOG + CNN & 90.40 & 72 \\ LBP + CNN & 91.07 & 67 \\ Improved CNN & 92.13 & 59 \\ Proposed Method & 93.60 & 48 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification accuracy and number of misclassifications performed by different methods when applied to VAIS dataset.