# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 3 Proposed Ship Classification Method
### Feature Extraction Based on Improved CNN  
#### 3.1.1 Motivation for Development of Improved CNN  
Although images within ship datasets can be easily affected by external factors, such as weather conditions, illumination, and viewing angles, each ship class possesses its own unique shape different from other classes. The key to correct ship classification, therefore, lies in the establishment of a CNN model capable of reliably identifying in-class differences and inter-class similarities. In addition, it is  
Figure 2: The flowchart of the proposed ship classification framework. also very important to select the appropriate network structure according to the number of the labeled samples. In this study, experiments were performed using the LeNet-5 network comprising few layers followed by fine-tuning of deep CNN networks (refer Section 3.4) to obtain the fine-AlexNet, fine-VGG-16, and fine-ResNet-18 versions for application to the self-built ship dataset (refer Section 4.1). The observed classification accuracy equaled only 89.99% when employing the LeNet-5 network while that obtained using the fine-AlexNet CNN was the highest (i.e., 93.22%). This indicates that feature-extraction characteristics of LeNet-5 network were not of the highest order, and consequently the corresponding ship-classification accuracy was not satisfactory. That said, although classification accuracy of the fine-AlexNet network equaled 93.22%, it comprises deeper layers and is easily susceptible to the occurrence of overfitting in a dataset with relatively few samples. Additionally, the number of parameters involved therein is relatively large, which in turn, affects the efficiency of the algorithm. Thus, classification accuracy of the said CNN can be further enhanced. In this study, changes have been effected in the AlexNet network structure to develop an improved ship-classification CNN that comprises shallow layers and few convolution kernels. The number of convolution and pooling layers has been reduced from five to four and three to two, respectively, to facilitate enhanced performance and reduced computational complexity. Additionally, the size of an image has been considered to be directly related to the convolution kernel being selected. Likewise, selection of the convolution-kernel size is related to whether image features can be effectively extracted. The size of the ship images considered in this study equaled 64 pixels \(\times\) 64 pixels, and after several experiments, convolution kernel sizes of 5 \(\times\) 5 and 3 \(\times\) 3 were considered for the extraction of ship features. The specific analysis can be described as follows. The convolution kernel size of typical classification networks, such as the LeNet-5, AlexNet, VGGNet, and ResNet, were considered to obtain the different combinations of kernel sizes listed in Table 1 (all other parameters are the same), wherein numbers 1-4 represent the four combination cases, respectively. Figure 3 depicts the classification accuracy for each ship class (refer Section 4.1) corresponding to the said four cases. As depicted in Figure 3, the classification accuracy of the container ship was observed to be the lowest in the first case, whereas that of a sailing ship was the lowest in the fourth case. Classification \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multicolumn{4}{c}{**Kernel Size**} \\ \cline{2-5}  & **1** & **2** & **3** & **4** \\ \hline Conv1 & \(7\times 7\) & \(5\times 5\) & \(5\times 5\) & \(3\times 3\) \\ Conv2 & \(3\times 3\) & \(3\times 3\) & \(3\times 3\) & \(3\times 3\) \\ Conv3 & \(3\times 3\) & \(3\times 3\) & \(3\times 3\) & \(3\times 3\) \\ Conv4 & \(3\times 3\) & \(3\times 3\) & \(5\times 5\) & \(3\times 3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Combination of different convolution kernels.