E2E-SGRWNet: A Stage-Guided Multi-Task Network for High-Precision River-Width Estimation.
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| Title: | E2E-SGRWNet: A Stage-Guided Multi-Task Network for High-Precision River-Width Estimation. |
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| Authors: | Zheng, Xinting1 (AUTHOR), Zhang, Guo2 (AUTHOR), Zhu, Chunyang2,3 (AUTHOR) chunyangzhu@whu.edu.cn, Cui, Hao1,2 (AUTHOR), Gao, Gui2,3 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 6, p894. 30p. |
| Subjects: | Optical remote sensing, Parameter estimation, Machine learning, Artificial neural networks, Image processing, Regression analysis, Image segmentation, Remote sensing |
| Abstract: | Highlights: What are the main findings? This study proposes E2E-SGRWNet, a stage-guided multi-task network that enables end-to-end estimation of river width from high-resolution optical remote sensing imagery through a stage-wise guidance strategy. On the self-constructed RiverWidth-HR Dataset, E2E-SGRWNet achieves state-of-the-art river-width estimation accuracy, with an MAE of 3.428 m and an RMSE of 4.362 m, outperforming existing multi-stage river-width extraction methods. What are the implications of the main findings? The proposed method provides a simpler and more robust solution for automated river-width estimation, effectively avoiding the error accumulation associated with traditional multi-stage pipelines. The stage-wise guidance strategy offers a generalizable modeling paradigm for other remote sensing-based geometric parameter regression tasks. Accurate estimation of river width is of great importance for hydrological analysis and water-related applications. However, existing remote sensing-based river-width extraction methods are often complex and prone to error accumulation due to their multi-step processing pipelines. To address these limitations, this study proposes an end-to-end stage-guided multi-task network for river-width estimation (E2E-SGRWNet), which directly regresses continuous river-width values from optical remote sensing imagery. The model adopts a stage-wise guidance strategy that progressively incorporates river spatial semantic information and geometric structural information to effectively guide the learning of river width. At the task-guidance level, a cascaded multi-task framework is designed according to the dependency relationships among tasks, in which river-mask segmentation and centerline extraction serve as auxiliary tasks to guide river-width regression. At the feature-guidance level, a cross-branch feature fusion mechanism is introduced to fully exploit multi-scale spatial semantic features and geometric structural features, thereby jointly guiding fine-grained river-width regression. The experimental results on the self-constructed RiverWidth-HR Dataset show that E2E-SGRWNet reduces the mean absolute error (MAE) and Root-Mean-Square error (RMSE) by 1.1 % and 3.8 % , respectively, compared with DeepRivWidth, the strongest existing multi-stage river-width estimation baseline. Overall, E2E-SGRWNet provides a concise and robust solution for high-precision, automated river-width estimation and offers new insights into end-to-end geometric parameter regression from remote sensing imagery. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? This study proposes E2E-SGRWNet, a stage-guided multi-task network that enables end-to-end estimation of river width from high-resolution optical remote sensing imagery through a stage-wise guidance strategy. On the self-constructed RiverWidth-HR Dataset, E2E-SGRWNet achieves state-of-the-art river-width estimation accuracy, with an MAE of 3.428 m and an RMSE of 4.362 m, outperforming existing multi-stage river-width extraction methods. What are the implications of the main findings? The proposed method provides a simpler and more robust solution for automated river-width estimation, effectively avoiding the error accumulation associated with traditional multi-stage pipelines. The stage-wise guidance strategy offers a generalizable modeling paradigm for other remote sensing-based geometric parameter regression tasks. Accurate estimation of river width is of great importance for hydrological analysis and water-related applications. However, existing remote sensing-based river-width extraction methods are often complex and prone to error accumulation due to their multi-step processing pipelines. To address these limitations, this study proposes an end-to-end stage-guided multi-task network for river-width estimation (E2E-SGRWNet), which directly regresses continuous river-width values from optical remote sensing imagery. The model adopts a stage-wise guidance strategy that progressively incorporates river spatial semantic information and geometric structural information to effectively guide the learning of river width. At the task-guidance level, a cascaded multi-task framework is designed according to the dependency relationships among tasks, in which river-mask segmentation and centerline extraction serve as auxiliary tasks to guide river-width regression. At the feature-guidance level, a cross-branch feature fusion mechanism is introduced to fully exploit multi-scale spatial semantic features and geometric structural features, thereby jointly guiding fine-grained river-width regression. The experimental results on the self-constructed RiverWidth-HR Dataset show that E2E-SGRWNet reduces the mean absolute error (MAE) and Root-Mean-Square error (RMSE) by 1.1 % and 3.8 % , respectively, compared with DeepRivWidth, the strongest existing multi-stage river-width estimation baseline. Overall, E2E-SGRWNet provides a concise and robust solution for high-precision, automated river-width estimation and offers new insights into end-to-end geometric parameter regression from remote sensing imagery. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18060894 |
