# Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy
## 5 Dataset Construction and Experimental Results
### Coarse Focusing-Classification Experiment  
Real spaceborne SAR data from multiple satellites, including Gaofen-3 [38], HISEA-1, Umbra, and Capella, are utilized to construct a coarse classification dataset. The dataset of ship target SAR images is categorized into three classes: aircraft carriers, other military ships, and civilian ships. The dataset for coarse classification is split into training, validation, and testing sets at a ratio of 0.7:0.2:0.1, as shown in Table 4.  
The coarse classification network is designed based on the improved ResNet-101 architecture. The training process consisted of 200 epochs, including 50 epochs of frozen backbone network training and 150 epochs of standard fine-tuning. The stochastic gradient descent (SGD) optimizer is employed to generate a non-linear descent learning rate. This optimization strategy initially uses higher learning rates to accelerate model convergence. Then lower learning rates are used in later stages to smooth the fitting process and achieve an optimal learning performance. The range of the learning rate is [0.0001, 0.01]. The loss curve during the training process is illustrated in Figure 16.  
When the backbone network was frozen, the loss of training and validation sets decreased smoothly, which indicated that the generalization performance of the model was preserved to some degree. After unfreezing the backbone network, both the training and validation sets converge to a relatively flat range. The optimal weights retained by the network are utilized to test the testing set. The recall and precision rates are shown in Figure 17.  
The statistical results show that the average recall rate of targets in the coarse-classification network can reach 87.5%, and the average recognition precision can reach 88.2%. The category of aircraft carriers has the lowest recall rate of 77%, while the category of other military ships has the lowest precision rate of 80%. This proves that the proposed coarse-classification network can effectively distinguish three types of targets, and there is no overfitting to the validation set. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target Category** & **Training Set** & **Validation Set** & **Testing Set** \\ \hline Aircraft carriers & 190 & 54 & 27 \\ Other military ships & 269 & 77 & 39 \\ Civilian ships & 294 & 84 & 42 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dataset for the coarse classification network. Figure 16: Loss curve of the coarse classification network. Simulated MEO SAR ship data for these three categories are employed to evaluate the generalization of the first-level model. In total, 10 aircraft carriers, 20 other military ships, and 30 civilian ships are selected for classification experiments. Figure 18 demonstrates a simulated range-compressed SAR image for a destroyer. The coarse-focusing method in Section 3.1 is performed on the range-compressed data. Figure 19a,c present two SAR images processed by a traditional imaging algorithm without motion error compensation. Figure 19b,d show the corresponding SAR images after coarse focusing. It can be seen that, after coarse focusing, the imaging quality has improved to some degree. The outline of the ship has been roughly restored. Although the details of the ship are still unclear, the size and shape features are still sufficient to support coarse classification. Figure 19: Simulated MEO SAR images with and without coarse focusing: (**a**) SAR image of a destroyer processed by a traditional image-focusing algorithm without motion error compensation; (**b**) SAR image of a destroyer with coarse focusing; (**c**) SAR image of a bulk carrier processed by a traditional image-focusing algorithm without motion error compensation; (**d**) SAR image of a bulk carrier with coarse focusing. Figure 17: Evaluation results of the testing set for the coarse classification network: (**a**) recall rate; (**b**) precision rate. Figure 18: Simulated MEO SAR data of a ship target in range-compressed domain.