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] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 192591384 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: E2E-SGRWNet: A Stage-Guided Multi-Task Network for High-Precision River-Width Estimation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zheng%2C+Xinting%22">Zheng, Xinting</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Guo%22">Zhang, Guo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Chunyang%22">Zhu, Chunyang</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> chunyangzhu@whu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Cui%2C+Hao%22">Cui, Hao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Gui%22">Gao, Gui</searchLink><relatesTo>2,3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Mar2026, Vol. 18 Issue 6, p894. 30p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Optical+remote+sensing%22">Optical remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18060894 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 894 Subjects: – SubjectFull: Optical remote sensing Type: general – SubjectFull: Parameter estimation Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Image processing Type: general – SubjectFull: Regression analysis Type: general – SubjectFull: Image segmentation Type: general – SubjectFull: Remote sensing Type: general Titles: – TitleFull: E2E-SGRWNet: A Stage-Guided Multi-Task Network for High-Precision River-Width Estimation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zheng, Xinting – PersonEntity: Name: NameFull: Zhang, Guo – PersonEntity: Name: NameFull: Zhu, Chunyang – PersonEntity: Name: NameFull: Cui, Hao – PersonEntity: Name: NameFull: Gao, Gui IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 6 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
