A Robust Deep Learning Ensemble Framework for Waterbody Detection Using High-Resolution X-Band SAR Under Data-Constrained Conditions

초록

Highlights What are the main findings? The performance of waterbody segmentation using deep learning models can be improved by incorporating land cover maps and topography information, such as slope and the Height Above Nearest Drainage (HAND), alongside high-resolution X-band SAR images. An ensemble of deep learning models provided moderate Intersection over Union (IoU) gains over the best single model but offered critical operational advantages in Precision-Recall balance and prediction consistency. What are the implications of the main findings? Multi-modal data integration for waterbody detection: Our results suggest that incorporating auxiliary geospatial layers (e.g., topography and land cover), when available and sufficiently reliable, can effectively reduce sensitivity to ambiguity arising from any single sensor modality in waterbody detection, particularly in complex terrain like Republic of Korea. Ensemble modeling for operational stability: For applications where consistent performance is critical (e.g., flood risk assessment), combining complementary architectures can help mitigate model-specific inductive biases. Although the quantitative gains in this study were moderate, ensemble aggregation improved the Precision-Recall balance and prediction consistency when adopting Optimized Weights via systematic grid search.Highlights What are the main findings? The performance of waterbody segmentation using deep learning models can be improved by incorporating land cover maps and topography information, such as slope and the Height Above Nearest Drainage (HAND), alongside high-resolution X-band SAR images. An ensemble of deep learning models provided moderate Intersection over Union (IoU) gains over the best single model but offered critical operational advantages in Precision-Recall balance and prediction consistency. What are the implications of the main findings? Multi-modal data integration for waterbody detection: Our results suggest that incorporating auxiliary geospatial layers (e.g., topography and land cover), when available and sufficiently reliable, can effectively reduce sensitivity to ambiguity arising from any single sensor modality in waterbody detection, particularly in complex terrain like Republic of Korea. Ensemble modeling for operational stability: For applications where consistent performance is critical (e.g., flood risk assessment), combining complementary architectures can help mitigate model-specific inductive biases. Although the quantitative gains in this study were moderate, ensemble aggregation improved the Precision-Recall balance and prediction consistency when adopting Optimized Weights via systematic grid search.Abstract Accurate delineation of inland waterbodies is critical for applications such as hydrological monitoring, disaster response preparedness and response, and environmental management. While optical satellite imagery is hindered by cloud cover or low-light conditions, Synthetic Aperture Radar (SAR) provides consistent surface observations regardless of weather or illumination. This study introduces a deep learning-based ensemble framework for precise inland waterbody detection using high-resolution X-band Capella SAR imagery. To improve the discrimination of water from spectrally similar non-water surfaces (e.g., roads and urban structures), an 8-channel input configuration was developed by incorporating auxiliary geospatial features such as height above nearest drainage (HAND), slope, and land cover classification. Four advanced deep learning segmentation models-Proportional-Integral-Derivative Network (PIDNet), Mask2Former, Swin Transformer, and Kernel Network (K-Net)-were systematically evaluated via cross-validation. Their outputs were combined using a weighted average ensemble strategy. The proposed ensemble model achieved an Intersection over Union (IoU) of 0.9422 and an F1-score of 0.9703 in blind testing, indicating high accuracy. While the ensemble gains over the best single model (IoU: 0.9371) were moderate, the enhanced operational reliability through balanced Precision-Recall performance provides significant practical value for flood and water resource monitoring with high-resolution SAR imagery, particularly under data-constrained commercial satellite platforms.

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