# 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
Then fine focusing is performed, and the well-trained fine-classification network is used to make the final classification. Figure 28 shows the multi-level focusing-classification results for simulated MEO SAR data corresponding to six types of ships. After coarse classification, the targets are divided into aircraft carriers, other military ships, and civilian ships. For aircraft carriers and other military ships, image slices of the bow are extracted. For civilian ships, image slices of the midship area are extracted. After fine focusing, the quality of these image slices is improved with enhanced structural details. Fine classification is then performed to achieve final recognition of aircraft carriers, destroyers, cruisers, bulk carriers, tankers, and container ships. The precision of the whole multi-level refocusing-classification network is shown in Table 8. The experimental results show that the proposed network can effectively achieve classification of six types of ship targets from simulated MEO SAR data. To further demonstrate the efficiency of the proposed method, its computational efficiency was compared with that of a traditional single-stage strategy of global fine focusing and fine classification. The experiments were both conducted on a computer with Intel\({}^{1}\) Core\({}^{1}\) \(\forall\)-10750H [EMAIL_ADDRESS] GHz, 64-GB RAM and NVIDIA RTX 3060 GPU (Dell, Round Rock, TX, USA), and 12-GB video memory. The time consumption of the two strategies at different steps is shown in Table 9. It can be seen that the computational cost generated by fine focusing accounts for the main part of the overall computational cost. The computational cost of the single-stage strategy is much higher than that of the proposed two-level strategy method. Under MEO SAR imaging conditions, defocusing caused by ship movements is very serious, due to the long synthetic aperture time. Coarse focusing can quickly estimate and compensate for errors introduced by ship sailing velocity. Figure 27: Evaluation results of the precision rate for the fine-classification network: (**a**) aircraft carrier confirmation network; (**b**) military ship fine-classification network; (**c**) civilian ship fine-classification network. Figure 26: Evaluation results of the recall rate for the fine-classification network: (**a**) aircraft carrier confirmation network; (**b**) military ship fine-classification network; (**c**) civilian ship fine-classification network. However, the fine-grained features of the target still cannot be accurately restored, making it difficult to directly achieve the expected target classification. On the other hand, the computational load generated by directly performing fine-focusing processing on all data is enormous, which will increase the computational burden of the entire process. Therefore, to reduce computational complexity in fine focusing, only local regions exhibiting typical target features are selected for fine focusing. And the coarse classification results from the previous stage can help locate local areas that require fine processing. The experimental and analytical results demonstrate that the proposed method can effectively improve the classification efficiency of MEO SAR ship data compared with a conventional method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target Category** & **Number of Test Data Points** & **Number of Correct Data Points** & **Precision** & **Average Precision** \\ \hline Aircraft carriers & 10 & 7 & 0.7 & \\ Destroyers & 10 & 7 & 0.7 & \\ Cruisers & 10 & 7 & 0.7 & \\ Bulk carriers & 10 & 9 & 0.9 & \\ Tankers & 10 & 10 & 1.0 & \\ Container ships & 10 & 9 & 0.9 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation results from the simulated data. Figure 28: Multi-level focusing-classification results for simulated MEO SAR data.