Self-Supervised Reservoir Water Area Detection Across Multi-Source Optical Imagery.
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| Title: | Self-Supervised Reservoir Water Area Detection Across Multi-Source Optical Imagery. |
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| Authors: | Mo, Guiyan1 (AUTHOR), Yang, Qing2,3 (AUTHOR) qy767@stanford.edu, Zhou, Xiaofeng1,3 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 6, p918. 29p. |
| Subjects: | Optical remote sensing, Spectral sensitivity, Machine learning, Remote sensing |
| Abstract: | Highlights: What are the main findings? We develop a label-free Self-Supervised Water Detection (SWD) framework. It automates sample initialization using geo-spectral features and addresses spectral variability and surface complexity through per-scene adaptive learning. Consistent and transferable performance is demonstrated across 36 test cases (3 sensors × 6 reservoirs × 2 hydrological conditions). SWD achieves high cross-scale consistency (IoU ≥ 0.774), stable cross-region generalization (SD: 0.010), and accurate hydrological tracking (minimal bias variation, ΔRE < 1%), without manual labels. What are the implications of the main findings? The high cross-scale consistency of the framework allows for the seamless integration of historical and current satellite archives to reconstruct reliable long-term surface water extent in ungauged basins. Without the need for site/sensor-specific training and specialized hardware, the proposed framework provides a scalable solution for near-real-time monitoring of hydrological emergencies and large-scale water resource management. 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We develop a label-free Self-Supervised Water Detection (SWD) framework. It automates sample initialization using geo-spectral features and addresses spectral variability and surface complexity through per-scene adaptive learning. Consistent and transferable performance is demonstrated across 36 test cases (3 sensors × 6 reservoirs × 2 hydrological conditions). SWD achieves high cross-scale consistency (IoU ≥ 0.774), stable cross-region generalization (SD: 0.010), and accurate hydrological tracking (minimal bias variation, ΔRE < 1%), without manual labels. What are the implications of the main findings? The high cross-scale consistency of the framework allows for the seamless integration of historical and current satellite archives to reconstruct reliable long-term surface water extent in ungauged basins. Without the need for site/sensor-specific training and specialized hardware, the proposed framework provides a scalable solution for near-real-time monitoring of hydrological emergencies and large-scale water resource management. 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18060918 |
