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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18010059 |
