# Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy
## 5 Dataset Construction and Experimental Results
### Fine Focusing-Classification Experiment  
Real spaceborne SAR data from Gaofen-3, HSEA-1, Umbra, and Capella are utilized to construct the dataset for ship fine classification. The local region slices for the ship target are extracted as data samples. The target categories include aircraft carriers, destroyers, cruisers, bulk carriers, tankers, and container ships, as shown in Figure 20.  
The experimental dataset for fine classification is split into training, validation, and testing sets at a ratio of 0.7:0.2:0.1, as shown in Table 6. The fine-classification network is designed based on MobileNet-v3 architecture. To prevent potential overfitting that may arise from training small sample sizes with excessive epochs, we limited the training to 100 epochs and omitted backbone network freezing. A SGD optimizer is also employed to generate a non-linear descent learning rate with a range of [0.0001, 0.01]. The loss curves during the training process are shown in Figure 21. The loss curves illustrate that all three fine-classification networks converged to relatively flat regions in both the training and testing datasets after a comparatively small number of training epochs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target Category** & **Training Set** & **Validation Set** & **Testing Set** \\ \hline Aircraft carriers & 190 & 54 & 27 \\ Destroyers & 161 & 47 & 23 \\ Cruisers & 108 & 31 & 15 \\ Bulk carriers & 105 & 30 & 15 \\ Tankers & 82 & 26 & 13 \\ Container ships & 97 & 28 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Dataset for the fine classification network. Figure 20: Examples of fine classification experimental data: (**a**) aircraft carrier; (**b**) destroyer; (**c**) cruiser; (**d**) bulk carrier; (**e**) tankers; (**f**) container ship. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target Category** & **Number of Test** & **Number of Correct** & **Precision** & **Average Precision** \\ \hline Aircraft carriers & 10 & 8 & 80\% & \\ Other military ships & 20 & 17 & 85\% & 88.3\% \\ Civilian ships & 30 & 28 & 93.3\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Coarse-classification network evaluation results from the simulated data. To enhance the model's adaptability, multi-scale target slices, including full, \(1/2\), \(1/3\), and \(1/4\) of target regions, are utilized during the network training phase. During the network inference phase, it is necessary to select a suitable local slice extraction parameter. Among the three fine-classification scenarios, the aircraft carrier confirmation network is a binary classifier, the military ship classification network is a three-class classifier, and the civilian ship classification network is a four-class classifier. Therefore, taking the most complex civilian ship classification as an example, we evaluate the fine-classification network performance under different region extraction sizes (\(1/2\), \(1/3\), and \(1/4\)), as shown in Figures 22-24. To ensure rationality, classification testing is conducted on the same batch of testing sets with different extraction sizes, and the weights and corresponding parameters of the classification model remain unchanged. The classification precision of different slice sizes is shown in Table 7.  
Figure 23: Extracted image slices of a bulk carrier: (**a**) \(1/2\) extraction; (**b**) \(1/3\) extraction; (c) \(1/4\) extraction. Figure 21: Loss curves of the fine-classification networks: (**a**) aircraft carrier confirmation network loss; (**b**) military ship fine-classification network loss; (**c**) civilian ship fine-classification network loss. Figure 22: Extracted image slices of a container ship: (**a**) \(1/2\) extraction; (**b**) \(1/3\) extraction; (c) \(1/4\) extraction. Simulated MEO SAR ship data are employed to evaluate the generalization of the proposed multi-level recognition strategy. Based on the coarse classification results, local region slices are extracted from the coarsely focused images.