## 4 Experimental Setup  
### Dataset  
The Hephaestus dataset [37] is a comprehensive resource designed for the study of synthetic aperture radar (SAR) imaging. It encompasses a diverse collection of SAR imagery that includes complex environments, urban structures, and various natural terrains, providing ample data for training and validating deep learning models in SAR-based object detection and scene classification. The dataset emphasizes real-world variability and simulates challenging environmental conditions to enhance the robustness of SAR processing algorithms in practical applications. Additionally, Hephaestus includes annotated labels that facilitate the development of models focusing on distinguishing minute features in cluttered scenes, making it highly valuable for SAR-related research. The xView3-SAR dataset [38] is aimed at advancing maritime domain awareness through SAR imaging. It comprises numerous high-resolution images captured over diverse maritime environments, including open seas and coastal regions. The dataset includes detailed annotations of maritime vessels, enabling researchers to develop and test object detection models tailored to SAR data. xView3-SAR serves as a robust benchmark for SAR-based maritime monitoring applications, addressing both detection and classification of vessels under various atmospheric and sea conditions. Its high spatial resolution supports the extraction of fine-grained features necessary for accurate and reliable performance in SAR-based maritime surveillance. The ASF SAR dataset [39] is a versatile SAR dataset provided by the Alaska Satellite Facility, focusing on a wide range of geographical terrains. This dataset features images captured by various SAR sensors, enabling multi-sensor analysis and the assessment of model generalization across different SAR systems. ASF SAR dataset includes annotations for land cover classification, which support the development of deep learning models aimed at environmental monitoring and terrain analysis. Its diverse content spanning natural landscapes, forested areas, and urban settings makes it suitable for robust SAR model training across various real-world scenarios, thus contributing significantly to environmental research and monitoring. The SAR patch dataset [40] offers a collection of small high-resolution SAR image patches focusing on specific regions of interest. Designed for tasks like feature extraction and patch-based classification, it provides targeted data that enable models to learn distinct features of objects within SAR imagery. Each patch includes detailed annotations, enabling precise object recognition and classification in complex SAR scenes. The SAR patch dataset is particularly valuable for applications in fine-grained object recognition within SAR imagery, serving as a critical dataset for the development of algorithms that require localized focus in large SAR data environments. Data preprocessing was conducted to standardize input data and enhance model performance. Normalization was applied to ensure consistency across datasets by aligning the mean and standard deviation of each dataset with those of the respective SAR sensors. The normalized data are calculated as  
\[X_{\text{norm}}=\frac{X-\mu}{\sigma},\]  
where \(X\) represents the raw input data, \(\mu\) is the mean, and \(\sigma\) is the standard deviation. Augmentation techniques, including random rotations, flips, and Gaussian noise injection, were used to simulate real-world conditions and improve the model's robustness to noise and variations. For datasets like SAR patches, high-resolution patches were extracted by segmenting large images into smaller fixed-size regions, often with overlapping areas to preserve spatial continuity. Scaling and resizing operations ensured that all input data met the dimensional requirements of the model architectures, particularly for Transformerbased designs. These preprocessing steps provided high-quality input data, essential for achieving reliable and robust model performance.