## 4 Experimental Setup
### Ablation Study  
The ablation study results, illustrated in Tables 3 and 4, highlight the impact of various model components on overall performance across the Hephaestus, xView3-SAR, ASF SAR, and SAR patch datasets. Each tested variant--denoted as \"w./o. Decomposition Module\", \"w./o. Graph Convolution Module\", and \"w./o. Temporal Sequence Modeling with Enhanced Predictive Power\"--excludes a specific module, while \"Full Model (Ours)\" represents the complete architecture, demonstrating the contributions of each component to the model's effectiveness in SAR-based time-series prediction tasks (as shown in Figures 7 and  
In the Hephaestus dataset, removing Decomposition Module resulted in a noticeable performance decrease, with accuracy dropping from 94.15% to 89.32% and F1-score declining from 91.42% to 85.47%. Decomposition Module, designed for spatiotemporal attention, appears to be essential for capturing the fine-grained temporal dependencies unique to SAR time-series data. The absence of this module reduces the model's ability to accurately capture long-term dependencies, which are critical in scenarios involving complex spatial structures and subtle environmental changes in SAR imagery. Similar trends are observed in the xView3-SAR dataset, where the full model achieved an AUC of 92.45%, outperforming the AUC of 87.90% observed in the absence of Decomposition Module, further reinforcing its importance in enhancing precision and recall for maritime detection tasks. On the ASF SAR and SAR patch datasets, Graph Convolution Module's exclusion (w./o. Graph Convolution Module) showed a substantial reduction in performance metrics, indicating its significance in fine-grained feature extraction across diverse terrains. Specifically, accuracy decreased from 94.87% to 91.67% on the ASF SAR dataset. Module B, tailored for multi-scale feature extraction, improves the model's adaptability to varied scales in SAR patches, enabling effective differentiation of environmental and object textures. The presence of Graph Convolution Module facilitates more accurate object localization and classification, critical for scenarios where detailed terrain analysis is required. The SAR patch dataset showed a similar trend, where the full model achieved a recall of 91.75% compared to 88.14% without Graph Convolution Module, underscoring its role in refining object classification precision within SAR imagery. Finally, Temporal Sequence Modeling with Enhanced Predictive Power, responsible for adaptive feature selection, proves to be crucial for enhancing the model's robustness across datasets. Excluding this module (w./o. Temporal Sequence Modeling with Enhanced Predictive Power) resulted in a performance drop, particularly in F1-score and AUC, which are essential metrics for assessing the balance between precision and recall. On the Hephaestus dataset, the F1-score of the full model was 91.42%, whereas it dropped to  
Figure 8: Ablation study of our method on ASF SAR dataset and SAR patch datasets (TSMEPP: Temporal Sequence Modeling with Enhanced Predictive Power). 88.02% without Temporal Sequence Modeling with Enhanced Predictive Power, indicating the importance of adaptive selection in achieving optimal feature representation. This module enables the model to adapt dynamically to varying image complexities, thus enabling it to handle intricate SAR data variations efficiently. On the ASF SAR dataset, Temporal Sequence Modeling with Enhanced Predictive Power's absence reduced AUC from 93.98% to 91.47%, affirming its contribution to maintaining model stability across challenging SAR scenes. To validate the robustness of the proposed approach under varying railway conditions and different environmental scenarios, we conducted an extensive evaluation using multiple datasets representing diverse railway terrains and weather conditions. The model was trained on a mixed dataset encompassing all conditions and tested on individual subsets to assess its adaptability.