## 4 Experimental Setup
### Comparison with SOTA Methods
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**ASF SAR Dataset**} & \multicolumn{4}{c}{**SAR Patch Dataset**} \\ \cline{2-9}  & **Accuracy** & **Recall** & **F1-Score** & **AUC** & **Accuracy** & **Recall** & **F1-Score** & **AUC** \\ \hline LSTM [40] & 88.27 \(\pm\) 0.03 & 86.14 \(\pm\) 0.02 & 84.39 \(\pm\) 0.02 & 89.32 \(\pm\) 0.03 & 87.42 \(\pm\) 0.02 & 84.52 \(\pm\) 0.02 & 83.58 \(\pm\) 0.02 & 88.21 \(\pm\) 0.03 \\ GRU [41] & 89.91 \(\pm\) 0.02 & 88.05 \(\pm\) 0.02 & 86.62 \(\pm\) 0.02 & 90.17 \(\pm\) 0.03 & 88.73 \(\pm\) 0.03 & 85.89 \(\pm\) 0.02 & 84.74 \(\pm\) 0.02 & 89.56 \(\pm\) 0.02 \\ Transformer [42] & 91.47 \(\pm\) 0.03 & 89.78 \(\pm\) 0.02 & 88.23 \(\pm\) 0.02 & 91.05 \(\pm\) 0.03 & 90.24 \(\pm\) 0.03 & 87.12 \(\pm\) 0.02 & 86.02 \(\pm\) 0.02 & 90.43 \(\pm\) 0.03 \\ TCN [43] & 89.83 \(\pm\) 0.02 & 87.24 \(\pm\) 0.02 & 85.82 \(\pm\) 0.02 & 89.92 \(\pm\) 0.03 & 89.17 \(\pm\) 0.02 & 86.53 \(\pm\) 0.02 & 85.29 \(\pm\) 0.02 & 89.14 \(\pm\) 0.03 \\ InceptionTime [44] & 92.18 \(\pm\) 0.03 & 90.34 \(\pm\) 0.02 & 89.07 \(\pm\) 0.02 & 91.68 \(\pm\) 0.03 & 90.97 \(\pm\) 0.03 & 88.24 \(\pm\) 0.02 & 87.02 \(\pm\) 0.02 & 90.85 \(\pm\) 0.02 \\ ResNet [45] & 90.35 \(\pm\) 0.02 & 88.56 \(\pm\) 0.02 & 87.19 \(\pm\) 0.02 & 90.12 \(\pm\) 0.03 & 89.87 \(\pm\) 0.02 & 87.21 \(\pm\) 0.02 & 86.04 \(\pm\) 0.02 & 89.76 \(\pm\) 0.03 \\ Ours (Proposed Model) & **94.03 \(\pm\) 0.02** & **92.47 \(\pm\) 0.02** & **91.18 \(\pm\) 0.02** & **93.52 \(\pm\) 0.03** & **93.67 \(\pm\) 0.03** & **91.43 \(\pm\) 0.02** & **90.05 \(\pm\) 0.02** & **92.14 \(\pm\) 0.02** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of proposed model with SOTA methods on ASF SAR and SAR patch datasets for time-series prediction. Bold indicates the best value. The comparative advantage of the proposed model is also apparent in the xView3-SAR dataset, where it achieved an accuracy of 92.48% and an AUC of 91.27%. This performance is attributed to our model's ability to process fine-grained maritime features, which are essential for distinguishing small-scale vessel variations under complex sea conditions. In contrast, models such as Transformer and ResNet, while generally effective for visual tasks, struggled to match the level of precision provided by our proposed method, especially in recall and F1-score metrics. This suggests that our model's specific design, optimized for SAR data characteristics, is advantageous for high-resolution maritime applications as it better leverages the rich feature space available in SAR imagery. Figures 5 and 6 further validate the robustness of the proposed model in environmental and terrain classification, as shown in the ASF SAR and SAR patch datasets. Here, our model demonstrated remarkable gains, achieving 94.03% and 93.67% accuracy, respectively, values that are significantly higher than those of the SOTA models. This improvement is particularly notable in recall and AUC metrics, where high values indicate the model's reliable detection of small objects in cluttered SAR scenes. These results can be attributed to the model's advanced feature extraction capabilities, supported by its multi-head attention components, which enable a thorough contextual understanding across patch-based and full-image resolutions. Compared to conventional architectures, our approach is more adaptable to variations in SAR patch composition and structure, effectively distinguishing between diverse terrain elements. The considerable advancements presented by our model are also evident in its ability to generalize across datasets. While models such as InceptionTime and Transformer show competitive performance on specific datasets, their overall generalization falls short compared to the proposed model, which consistently outperforms across different SAR datasets and evaluation metrics.