RECOGNITION OF THE WATER SURFACE ACCORDING TO ICEYE DATA USING MACHINE LEARNING
DOI:
https://doi.org/10.37943/23KUWI4163Keywords:
ICEYE, synthetic aperture radar, machine learning, Random Forest, decision trees, water recognitionAbstract
The growing frequency of floods and the resulting socio-economic losses highlight the need for accurate and automated tools for detecting and monitoring water surfaces. This study presents a methodology for automatic water surface recognition based on high-resolution ICEYE synthetic aperture radar (SAR) data. The algorithm is implemented in the Google Earth Engine environment and uses the Random Forest machine-learning model trained on manually labeled “water” and “land” classes derived directly from the radar imagery. Preprocessing, performed in ESA SNAP, included radiometric calibration, Range-Doppler terrain correction, and speckle filtering to ensure accurate backscatter representation. The trained model was applied to ICEYE VV-polarized images acquired over Uralsk, Kazakhstan, on April 20–21, 2024, during a major regional flood. To validate the results, the Random Forest–derived masks were compared with those obtained using traditional methods such as Otsu and fixed-threshold classification, as well as optical masks generated from Sentinel-2 NDWI and MNDWI indices. Quantitative evaluation showed an overall accuracy of 76.8 % and a kappa coefficient of 0.535, while the area under the ROC curve (AUC = 0.91) indicated strong discriminatory capability. The Random Forest model demonstrated greater spatial precision and reduced false-positive mapping compared to threshold-based methods, confirming its suitability for operational flood monitoring. The proposed approach highlights the potential of ICEYE data for near-real-time water surface mapping, especially under cloud-covered conditions where optical sensors are ineffective. Moreover, the developed workflow ensures reproducibility and can be integrated into automated flood-response systems for rapid situation assessment. In the future, incorporating additional polarimetric and texture features is expected to further enhance model performance and extend its applicability to diverse hydrological environments.
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