Remote Sensing Interpretation of Soil Elements via a Feature-Reinforcement Multiscale-Fusion Network.
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| Title: | Remote Sensing Interpretation of Soil Elements via a Feature-Reinforcement Multiscale-Fusion Network. |
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| Authors: | Zhang, Zhijun1 (AUTHOR), Tian, Mingliang2 (AUTHOR), Gao, Wenbo1 (AUTHOR), Wang, Yanliang1,2 (AUTHOR), Zhang, Fengshan1 (AUTHOR), Wang, Mo1 (AUTHOR) wangmo@mail.cgs.gov.cn |
| Source: | Remote Sensing. Jan2026, Vol. 18 Issue 1, p171. 21p. |
| Subjects: | Remote sensing, Geological mapping, Deep learning, Remote-sensing images, Calibration, Soils |
| Abstract: | Highlights: What are the main findings? RFMFFNet improves lithological interpretation by combining adaptive feature calibration with an efficient multiscale fusion strategy. The method achieves notable performance gains, improving oPA/mIoU by 2.4%/2.6% on Landsat-8 and 4.3%/4.1% on Sentinel-2. What are the implications of the main findings? The framework improves the reliability of geological element extraction in heterogeneous surface conditions with vegetation cover and complex geomorphology. It offers an effective and transferable solution for large-scale geological mapping and environmental monitoring using multispectral satellite imagery. Accurately delineating soil elements from satellite imagery is fundamental for regional geological mapping and survey. However, vegetation cover and complex geomorphological conditions often obscure diagnostic surface information, weakening the visibility of key geological features. Additionally, long-term tectonic deformation and weathering processes reshape the spatial organization of soil elements, resulting in substantial within-class variability, inter-class spectral overlap, and fragmented structural patterns—all of which hinder reliable segmentation performance for conventional deep learning approaches. To mitigate these challenges, this study introduces a Reinforced Feature and Multiscale Feature Fusion Network (RFMFFNet) tailored for semantic interpretation of soil elements. The model incorporates a rectangular calibration attention (RCA) module into a ResNet101 backbone to recalibrate feature responses in critical regions, thereby improving scale adaptability and the preservation of fine geological structures. A complementary multiscale feature fusion (MFF) component is further designed by combining sparse self-attention with pyramid pooling, enabling richer context aggregation while reducing computational redundancy. Comprehensive experiments on the Landsat-8 and Sentinel-2 datasets verify the effectiveness of the proposed framework. RFMFFNet consistently achieves superior segmentation performance compared with several mainstream deep learning models. On the Landsat-8 dataset, the oPA and mIoU increase by 2.4% and 2.6%, respectively; on the Sentinel-2 dataset, the corresponding improvements reach 4.3% and 4.1%. [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: 190787391 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Remote Sensing Interpretation of Soil Elements via a Feature-Reinforcement Multiscale-Fusion Network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Zhijun%22">Zhang, Zhijun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tian%2C+Mingliang%22">Tian, Mingliang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Wenbo%22">Gao, Wenbo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yanliang%22">Wang, Yanliang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Fengshan%22">Zhang, Fengshan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Mo%22">Wang, Mo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wangmo@mail.cgs.gov.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jan2026, Vol. 18 Issue 1, p171. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Geological+mapping%22">Geological mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink><br /><searchLink fieldCode="DE" term="%22Soils%22">Soils</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? RFMFFNet improves lithological interpretation by combining adaptive feature calibration with an efficient multiscale fusion strategy. The method achieves notable performance gains, improving oPA/mIoU by 2.4%/2.6% on Landsat-8 and 4.3%/4.1% on Sentinel-2. What are the implications of the main findings? The framework improves the reliability of geological element extraction in heterogeneous surface conditions with vegetation cover and complex geomorphology. It offers an effective and transferable solution for large-scale geological mapping and environmental monitoring using multispectral satellite imagery. Accurately delineating soil elements from satellite imagery is fundamental for regional geological mapping and survey. However, vegetation cover and complex geomorphological conditions often obscure diagnostic surface information, weakening the visibility of key geological features. Additionally, long-term tectonic deformation and weathering processes reshape the spatial organization of soil elements, resulting in substantial within-class variability, inter-class spectral overlap, and fragmented structural patterns—all of which hinder reliable segmentation performance for conventional deep learning approaches. To mitigate these challenges, this study introduces a Reinforced Feature and Multiscale Feature Fusion Network (RFMFFNet) tailored for semantic interpretation of soil elements. The model incorporates a rectangular calibration attention (RCA) module into a ResNet101 backbone to recalibrate feature responses in critical regions, thereby improving scale adaptability and the preservation of fine geological structures. A complementary multiscale feature fusion (MFF) component is further designed by combining sparse self-attention with pyramid pooling, enabling richer context aggregation while reducing computational redundancy. Comprehensive experiments on the Landsat-8 and Sentinel-2 datasets verify the effectiveness of the proposed framework. RFMFFNet consistently achieves superior segmentation performance compared with several mainstream deep learning models. On the Landsat-8 dataset, the oPA and mIoU increase by 2.4% and 2.6%, respectively; on the Sentinel-2 dataset, the corresponding improvements reach 4.3% and 4.1%. [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/rs18010171 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 171 Subjects: – SubjectFull: Remote sensing Type: general – SubjectFull: Geological mapping Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Remote-sensing images Type: general – SubjectFull: Calibration Type: general – SubjectFull: Soils Type: general Titles: – TitleFull: Remote Sensing Interpretation of Soil Elements via a Feature-Reinforcement Multiscale-Fusion Network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Zhijun – PersonEntity: Name: NameFull: Tian, Mingliang – PersonEntity: Name: NameFull: Gao, Wenbo – PersonEntity: Name: NameFull: Wang, Yanliang – PersonEntity: Name: NameFull: Zhang, Fengshan – PersonEntity: Name: NameFull: Wang, Mo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 1 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
