Sub-Pixel Classification of Water Body from Sentinel-1 SAR Images Using Attention V-net

As a result of the increasing frequency and intensity of hydrological disasters such as floods and droughts due to climate change, the importance of stable and precise water body detection technology is growing. Although Sentinel-1 Synthetic Aperture Radar (SAR) imagery, which can provide wide-area observation, has the advantage of all-weather usability, it has limitations in detecting small-scale rivers or complex boundaries due to its relatively low spatial resolution. To overcome these limitations, this study applied an Attention V-net-based Single Image-based Super-Resolution model to evaluate water body detection performance. The proposed model showed excellent performance on the test dataset, achieving an Intersection over Union (IoU) of 0.879 and an F1-score of 0.936. A qualitative analysis also confirmed its ability to maintain river continuity and precisely restore boundary areas. A regional analysis showed that while the detection of main rivers and tributaries was stable in the Russia dataset, the Houston dataset, which has a mix of urban and suburban areas, showed instances where roads or agricultural fields were incorrectly classified as water bodies. This suggests that the model is influenced not only by the SAR signal but also by local land cover characteristics. It is expected that if these limitations are supplemented in the future through multi-temporal SAR analysis, optical-radar fusion, and post-processing corrections, this study can contribute to real-world water resource management and flood monitoring.