Balancing Accuracy and Efficiency: HWBENet for Water Body Extraction in Complex Rural Landscapes.
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| Title: | Balancing Accuracy and Efficiency: HWBENet for Water Body Extraction in Complex Rural Landscapes. |
|---|---|
| Authors: | Lei, Pengyu1 (AUTHOR), Zhang, Jiang2 (AUTHOR) t19990896@csuft.edu.cn, Yi, Jizheng1 (AUTHOR) |
| Source: | Remote Sensing. Nov2025, Vol. 17 Issue 22, p3711. 22p. |
| Subjects: | Environmental monitoring, Water analysis, Statistical accuracy, Deep learning, Edge detection (Image processing), Remote sensing, Mathematical optimization |
| Abstract: | Highlights: What are the main findings? A novel hybrid network called HWBENet is introduced, and it is specifically designed for water extraction in complex rural landscapes, achieving an excellent balance between accuracy and efficiency. The CIMM and ERM modules are proposed to improve the network's segmentation accuracy of fragmented water bodies and water body boundaries. What are the implications of the main findings? The introduction of HWBENet resolves the long-standing contradiction between accuracy and efficiency in the field of water body extraction from remote sensing images. As an efficient and accurate solution, HWBENet offers significant practical value for large-scale water ripple mapping in complex rural landscapes, meaning it can provide more reliable and timely water information for environmental monitoring. The accurate and timely extraction of water bodies from high-resolution remote sensing imagery is vital for environmental monitoring, yet segmenting small, scattered, and irregularly shaped water bodies in complex rural landscapes remains a persistent challenge. While state-of-the-art deep learning models have advanced segmentation accuracy, they often achieve this at the cost of substantial computational overhead, limiting their practical application for large-scale monitoring. To address this trade-off between precision and efficiency, this paper introduces HWBENet, a novel hybrid network for water body extraction. HWBENet is built upon a lightweight MobileNetV3 encoder to ensure computational efficiency while preserving strong feature extraction capabilities. Its core innovation lies in two specifically designed modules. First, the Contextual Information Mining Module (CIMM) is proposed to enhance the network's ability to learn and fuse both global scene-level context and fine-grained local details, which is crucial for identifying fragmented water bodies. Second, an Edge Refinement Module (ERM) is integrated into the decoder, which uniquely leverages transformer mechanisms to sharpen boundary details by effectively fusing prior feature information with up-sampled features. Extensive experiments on challenging rural water body datasets demonstrate that HWBENet strikes a superior balance between accuracy and computational cost. The experimental results validate the finding that HWBENet is an efficient, accurate, and scalable solution, offering significant practical value for large-scale hydrological mapping in complex rural environments. [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: 189674954 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Balancing Accuracy and Efficiency: HWBENet for Water Body Extraction in Complex Rural Landscapes. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lei%2C+Pengyu%22">Lei, Pengyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jiang%22">Zhang, Jiang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> t19990896@csuft.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yi%2C+Jizheng%22">Yi, Jizheng</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Nov2025, Vol. 17 Issue 22, p3711. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Environmental+monitoring%22">Environmental monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Water+analysis%22">Water analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+accuracy%22">Statistical accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+detection+%28Image+processing%29%22">Edge detection (Image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? A novel hybrid network called HWBENet is introduced, and it is specifically designed for water extraction in complex rural landscapes, achieving an excellent balance between accuracy and efficiency. The CIMM and ERM modules are proposed to improve the network's segmentation accuracy of fragmented water bodies and water body boundaries. What are the implications of the main findings? The introduction of HWBENet resolves the long-standing contradiction between accuracy and efficiency in the field of water body extraction from remote sensing images. As an efficient and accurate solution, HWBENet offers significant practical value for large-scale water ripple mapping in complex rural landscapes, meaning it can provide more reliable and timely water information for environmental monitoring. The accurate and timely extraction of water bodies from high-resolution remote sensing imagery is vital for environmental monitoring, yet segmenting small, scattered, and irregularly shaped water bodies in complex rural landscapes remains a persistent challenge. While state-of-the-art deep learning models have advanced segmentation accuracy, they often achieve this at the cost of substantial computational overhead, limiting their practical application for large-scale monitoring. To address this trade-off between precision and efficiency, this paper introduces HWBENet, a novel hybrid network for water body extraction. HWBENet is built upon a lightweight MobileNetV3 encoder to ensure computational efficiency while preserving strong feature extraction capabilities. Its core innovation lies in two specifically designed modules. First, the Contextual Information Mining Module (CIMM) is proposed to enhance the network's ability to learn and fuse both global scene-level context and fine-grained local details, which is crucial for identifying fragmented water bodies. Second, an Edge Refinement Module (ERM) is integrated into the decoder, which uniquely leverages transformer mechanisms to sharpen boundary details by effectively fusing prior feature information with up-sampled features. Extensive experiments on challenging rural water body datasets demonstrate that HWBENet strikes a superior balance between accuracy and computational cost. The experimental results validate the finding that HWBENet is an efficient, accurate, and scalable solution, offering significant practical value for large-scale hydrological mapping in complex rural environments. [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/rs17223711 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 3711 Subjects: – SubjectFull: Environmental monitoring Type: general – SubjectFull: Water analysis Type: general – SubjectFull: Statistical accuracy Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Edge detection (Image processing) Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Mathematical optimization Type: general Titles: – TitleFull: Balancing Accuracy and Efficiency: HWBENet for Water Body Extraction in Complex Rural Landscapes. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lei, Pengyu – PersonEntity: Name: NameFull: Zhang, Jiang – PersonEntity: Name: NameFull: Yi, Jizheng IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 17 – Type: issue Value: 22 Titles: – TitleFull: Remote Sensing Type: main |
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