What question did this study set out to answer?

The primary aim is to develop an automated water detection framework that effectively addresses challenges in reservoir monitoring using multi-source optical imagery.

March 21, 2026Open Access

Self-Supervised Reservoir Water Area Detection Across Multi-Source Optical Imagery

Key Points

The primary aim is to develop an automated water detection framework that effectively addresses challenges in reservoir monitoring using multi-source optical imagery.
Developed a self-supervised water detection (SWD) framework for automated analysis.
Integrated spatial priors and spectral features for high-confidence sample derivation.
Applied Gaussian mixture models for representing spectral distribution.
Utilized superpixel-constrained region growing for shoreline refinement.
Validated across multiple sensors, reservoirs, and hydrological conditions.
Achieved high consistency with IoU ≥ 0.774 in cross-scale tests.
Maintained stable generalization with a standard deviation of 0.010 in cross-region transfers.
Captured water-level fluctuations with minimal bias variation (ΔRE < 1%).
Processing times ranged from 0.49 to 1.29 seconds per megapixel on standard CPUs.

Abstract

Reservoirs are critical infrastructure for water and energy security, and require accurate and timely monitoring of reservoir water extent to make informed decisions. Optical remote sensing provides frequent, large-area observations; however, automated water extraction is often complicated by dam operation and surface heterogeneity, which increase spectral variability. Supervised methods, though widely used, generally require manual labels and often perform poorly when transferred across sensors and regions, limiting operational deployment. In this paper, we develop a geo-spectral feature-guided Self-Supervised Water Detection (SWD) framework, an automated algorithm designed for multi-source optical imagery. SWD consists of two stages: pixel-level classification and object-level refinement. Initially, SWD integrates spatial priors with spectral features to automatically derive high-confidence samples, which are then utilized to parameterize Gaussian mixture model to represent multimodal spectral distribution throughout the image. Furthermore, superpixel-constrained region growing is applied to refine shoreline and ensure object-level consistency. We validated SWD across 36 test cases comprising three sensors, six reservoirs, and two hydrological conditions. Compared with Random Forest and U-Net, SWD achieved the best performance. Specifically, (1) in cross-scale tests, SWD achieved high consistency with IoU ≥ 0.774; (2) in cross-region transfers, SWD maintained stable generalization (SD: 0.010); and (3) in hydrological response assessments, SWD captured water-level fluctuations with minimal bias variation (ΔRE < 1%). In addition, SWD framework is computationally efficient, with processing times of 0.49–1.29 s/Mpx on a standard CPU. This study demonstrates that SWD effectively addresses spectral variability and surface complexity in reservoir water area detection across multi-source optical imagery. It operates without manual labels or model training, enabling automated, large-scale and multi-temporal reservoir water monitoring.

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Cite This Study

Mo et al. (Wed,) studied this question.

synapsesocial.com/papers/69be35a96e48c4981c674067 https://doi.org/https://doi.org/10.3390/rs18060918

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