LENet: A Semantic Segmentation Network for Complex Landforms in Remote Sensing Imagery via Axial Semantic Modeling and Deformation-Aware Compensation.
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| Title: | LENet: A Semantic Segmentation Network for Complex Landforms in Remote Sensing Imagery via Axial Semantic Modeling and Deformation-Aware Compensation. |
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| Authors: | Liu, Yaning1 (AUTHOR), Ren, Jing2 (AUTHOR) 15882126124@163.com, Wang, Jiakun1,2 (AUTHOR), Li, Shaoda1,2 (AUTHOR), Chen, Rui2 (AUTHOR), Zhong, Dongsheng1 (AUTHOR), Zhao, Wei2 (AUTHOR), Yang, Aiping2 (AUTHOR), Yang, Ronghao1 (AUTHOR) |
| Source: | Remote Sensing. Jan2026, Vol. 18 Issue 1, p59. 25p. |
| Subjects: | Remote sensing, Landforms, Image segmentation, Image processing software |
| Abstract: | Highlights: What are the main findings? Proposes LENet, a novel semantic segmentation network for complex landforms. Achieves state-of-the-art performance on PKLD and GVLM datasets with high robustness. What are the implications of the main findings? Introduces axial semantic modeling to enhance long-range contextual dependencies. Designs a Feature Expert Compensator (FEC) for deformation-aware intra-class fusion. Employs Cross Sparse Attention (CSA) to suppress background noise and refine details. Accurate semantic segmentation of complex landforms in remote sensing imagery is hindered by pronounced intra-class heterogeneity, blurred boundaries, and irregular geomorphic structures. To overcome these challenges, this study presents LENet (Landforms Expert Segmentation Net), a novel segmentation network that combines axial semantic modeling with deformation-aware compensation. LENet follows an encoder–decoder framework, where the decoder integrates three key modules: the Expert Enhancement Block (EEBlock) for capturing long-range dependencies along axial directions; the Feature Expert Compensator (FEC) employing deformable convolutions with channel–spatial decoupled weights to emphasize ambiguous intra-class regions; and the Cross-Sparse Attention (CSA) mechanism that suppresses background noise via multi-rate sparsity masks and enhances intra-class consistency through cosine-similarity weighting. Experiments conducted on the PKLD plateau karst and GVLM landslide datasets demonstrate that LENet achieves IoU scores of 70.39% and 80.95% and Recall values of 83.33% and 91.38%, surpassing eight state-of-the-art methods. These results confirm that LENet effectively balances global contextual understanding and local detail refinement, providing a robust and accurate solution for complex landform segmentation in 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: 190787279 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: LENet: A Semantic Segmentation Network for Complex Landforms in Remote Sensing Imagery via Axial Semantic Modeling and Deformation-Aware Compensation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Yaning%22">Liu, Yaning</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ren%2C+Jing%22">Ren, Jing</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> 15882126124@163.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Jiakun%22">Wang, Jiakun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Shaoda%22">Li, Shaoda</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Rui%22">Chen, Rui</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhong%2C+Dongsheng%22">Zhong, Dongsheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Wei%22">Zhao, Wei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Aiping%22">Yang, Aiping</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Ronghao%22">Yang, Ronghao</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jan2026, Vol. 18 Issue 1, p59. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Landforms%22">Landforms</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing+software%22">Image processing software</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? Proposes LENet, a novel semantic segmentation network for complex landforms. Achieves state-of-the-art performance on PKLD and GVLM datasets with high robustness. What are the implications of the main findings? Introduces axial semantic modeling to enhance long-range contextual dependencies. Designs a Feature Expert Compensator (FEC) for deformation-aware intra-class fusion. Employs Cross Sparse Attention (CSA) to suppress background noise and refine details. Accurate semantic segmentation of complex landforms in remote sensing imagery is hindered by pronounced intra-class heterogeneity, blurred boundaries, and irregular geomorphic structures. To overcome these challenges, this study presents LENet (Landforms Expert Segmentation Net), a novel segmentation network that combines axial semantic modeling with deformation-aware compensation. LENet follows an encoder–decoder framework, where the decoder integrates three key modules: the Expert Enhancement Block (EEBlock) for capturing long-range dependencies along axial directions; the Feature Expert Compensator (FEC) employing deformable convolutions with channel–spatial decoupled weights to emphasize ambiguous intra-class regions; and the Cross-Sparse Attention (CSA) mechanism that suppresses background noise via multi-rate sparsity masks and enhances intra-class consistency through cosine-similarity weighting. Experiments conducted on the PKLD plateau karst and GVLM landslide datasets demonstrate that LENet achieves IoU scores of 70.39% and 80.95% and Recall values of 83.33% and 91.38%, surpassing eight state-of-the-art methods. These results confirm that LENet effectively balances global contextual understanding and local detail refinement, providing a robust and accurate solution for complex landform segmentation in 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/rs18010059 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 59 Subjects: – SubjectFull: Remote sensing Type: general – SubjectFull: Landforms Type: general – SubjectFull: Image segmentation Type: general – SubjectFull: Image processing software Type: general Titles: – TitleFull: LENet: A Semantic Segmentation Network for Complex Landforms in Remote Sensing Imagery via Axial Semantic Modeling and Deformation-Aware Compensation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Yaning – PersonEntity: Name: NameFull: Ren, Jing – PersonEntity: Name: NameFull: Wang, Jiakun – PersonEntity: Name: NameFull: Li, Shaoda – PersonEntity: Name: NameFull: Chen, Rui – PersonEntity: Name: NameFull: Zhong, Dongsheng – PersonEntity: Name: NameFull: Zhao, Wei – PersonEntity: Name: NameFull: Yang, Aiping – PersonEntity: Name: NameFull: Yang, Ronghao 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 |
