# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 4 Experimental Results and Analysis  
### Experimental Datasets  
The first dataset used in this study corresponds to the self-built dataset, which contained 3000 RGB original images of different sizes. The said images were partly collected from ship-image databases available on websites of the China Shipping Service and Baidu. The remaining images were collected from the Yangtze River channel between the Zhonghua road and Wuhan wharfs, which define an inland waterway with the largest cargo volume in the world. The said ship images were collected during daytime between 9 am and 5 pm to ensure uniform illumination conditions during image collection. The acquisition area is depicted in Figure 12. The circulation of ships within the said region was large, and there not only existed bulk carriers but also passenger ships, and container ships. This facilitated collection of different types of ship images under different environments. Each image within the dataset was manually labeled as belonging to one of four classes--bulk carriers, container ships, passenger ships, and sailing boats. A few image samples are depicted in Figure 13. In order to improve the recognition ability, each image was rotated counterclockwise 15 degrees and mirrored image respectively to expand the dataset. Therefore, the self-built dataset contained 9000 ship images after augmentation. There were 7201 training images, accounting for roughly 80% of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Kernel Size** & **Filter Number** & **Stride** \\ \hline Conv1 & \(3\times 3\) & 32 & 1 \\ Max Pooling1 & \(2\times 2\) & - & 2 \\ Conv2 & \(3\times 3\) & 64 & 1 \\ [PERSON] & \(2\times 2\) & - & 2 \\ Conv3 & \(3\times 3\) & 64 & 1 \\ Conv4 & \(3\times 3\) & 128 & 1 \\ Conv5 & \(3\times 3\) & 256 & 1 \\ [PERSON] & \(2\times 2\) & - & 2 \\ FC1 & 4096 & - & - \\ FC2 & 4096 & - & - \\ FC3-Softmax & 4(6) & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Structure of fine-AlexNet network.  
The second dataset (i.e., VAIS dataset), which is the world's first publicly available dataset comprised of 2865 images (1623 visible and 1242 infrared), including 1088 corresponding pairs. These images were captured using a multimodal stereo camera rig. The dataset included six coarse-grained classes (or 15 fine-grained classes)--merchant ships (26 cargo ships and nine barges), medium passenger ships (11 ferries and four tour boats), sailing ships (41 sails up and 24 sails down), small boats (28 speedboats, six jet-skis, 25 small pleasure boats, and 13 large pleasure boats), 19 tugboats, and medium \"other\" ships (eight fishing and 14 medium other), as depicted in Figure 14. For each image within the dataset, bounding boxes were manually labeled. The area of visible bounding boxes occupied 644-4,478,952 pixels with corresponding mean and median values of 181,319 pixels and 9983 pixels, respectively. The dataset was divided into \"official\" training and testing parts. Since the authors were interested in generalization, all images were greedily assigned from each named ship to either partition. This resulted in the creation of 539 image pairs and 334 singletons for training and 549 image pairs and 358 singletons for testing. In this study, only the visible ship imagery was chosen. Table 6 lists the number of training and test samples. Each image was resized to 64 pixels \(\times\) 64 pixels via bicubic interpolation, which was performed in a manner similar to that described in a previous study [27].