# Classification of Marine Vessels with Multi-Feature Structure Fusion
## 4 Experimental Results and Analysis
### Experiments
The experimental results are shown in Table 5. From the results of Table 5, with the exception of the nighttime IR results, our method achieved the best recognition results for different weather conditions, different modal images, and multi-modal images, indicating that the feature fusion method of this paper has a strong robustness. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Visible**} & \multicolumn{2}{c}{**IR**} \\ \cline{2-5}  & **Train Time** & **Test Time** & **Train Time** & **Test Time** \\  & **in 873 Images** & **in 750 Images** & **in 539 Images** & **in 703 Images** \\ \hline VGG-19(relu5-4) + SVM & 31.02 & 0.03 & 94.74 & 0.03 \\ ResNet-152(pool5) + SVM & 1.74 & 0.004 & 1.58 & 0.005 \\ SFLPP [24] & 7.14 & 0.29 & 3.01 & 0.16 \\ SRDA [29] & 1.58 & 0.10 & 0.82 & 0.08 \\ The proposed SF-SRDA & 2.49 & 0.10 & 1.43 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Time consume among SF-SRDA and the base-line methods in the coarse-grained partition (second). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Feature**} & \multicolumn{2}{c}{**Daytime**} & \multicolumn{2}{c}{**Nighttime**} & \multirow{2}{*}{**IR**} \\ \cline{2-2} \cline{4-5}  & **Visible** & **IR** & **Visible + IR** & & \\ \hline \hline \multirow{2}{*}{Gnostic Field [5]} & 82.4\% & 58.7\% & 82.4\% & 51.9\% & - \\ CNN [5] & 81.9\% & 54.0\% & 82.1\% & 59.9\% & - \\ \multicolumn{1}{c}{Cosistic Field + CNN [5]} & 81.0\% & 56.8\% & 87.4\% & **61.0\%** & - \\ \multicolumn{1}{c}{Gabor + MS-CLBP [11]} & 77.73\% & - & - & - & - \\ \multicolumn{1}{c}{MFL (decision-level) + ELM [11]} & 85.07\% & - & - & - & - \\ \multicolumn{1}{c}{MFL (feature-level) + SVM [11]} & 85.33\% & - & - & - & - \\ \multicolumn{1}{c}{HOG + SVM [19]} & 71.87\% & - & - & - & - \\ \multicolumn{1}{c}{ME-CNN [19]} & 87.33\% & - & - & - & - \\ \multicolumn{1}{c}{ELM-CNN [20]} & - & - & - & - & 61.17\% \\ \multicolumn{1}{c}{LBP + SVM} & 76.27\% & - & - & - & 56.67\% \\ \multicolumn{1}{c}{HOG + SVM} & 72.40\% & - & - & - & 57.18\% \\ \multicolumn{1}{c}{SFLPP [24]} & 84.93\% & 70.67\% & 79.60\% & 46.75\% & 65.43\% \\ \multicolumn{1}{c}{SRDA [29]} & 86.93\% & 74.68\% & 86.52\% & 55.84\% & 70.56\% \\ \multicolumn{1}{c}{The proposed SF-SRDA} & **87.60\%** & **74.68\%** & **87.98\%** & 57.79\% & **70.98\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Coarse-grained results among SF-SRDA and the state-of-arts. To further validate the effectiveness of SF-SRDA, we conduct experiments in the case of fine-grained dataset with more categories, and compare with the two base-line methods. As shown in the experimental results in Table 6, we compared SF-SRDA with SFLPP and achieved better recognition results and great improvement in various cases. Compared with the SRDA method, SF-SRDA obtains better recognition results in cases of visible, IR, and paired images during the daytime. Only nighttime IR results are slightly lower than the SRDA method. The main discussion about the performance comparisons include the following: (1) The proposed SF-SRDA under the coarse-grained condition and SFLPP under the fine-grained condition, combining multi sensors (visible and IR) shows performance enhancement, while in some other cases, it shows performance degradation. The reason for this is that the IR image has a significantly lower resolution and a smaller size than the visible image. Figure 6 shows some examples of the IR image, thus IR information has little effect on enhancing recognition during the day. (2) The focus of this paper is on feature fusion. It can be seen from our experimental results that feature fusion is better than modal fusion. (3) In the nighttime IR image classification, the reason our method did not improve may be that the category information of the nighttime IR image is very blurred, and the image of the fine-grained classes makes almost no difference.