Remote Sensing Image Segmentation Network That Integrates Global–Local Multi-Scale Information with Deep and Shallow Features
As the spatial resolution of remote sensing images continues to increase, the complexity of the information they carry also grows. Remote sensing images are characterized by large imaging areas, scattered distributions of similar objects, intricate boundary shapes, and a high density of small object...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/11/1880 |
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| author | Nan Chen Ruiqi Yang Yili Zhao Qinling Dai Leiguang Wang |
| author_facet | Nan Chen Ruiqi Yang Yili Zhao Qinling Dai Leiguang Wang |
| author_sort | Nan Chen |
| collection | DOAJ |
| description | As the spatial resolution of remote sensing images continues to increase, the complexity of the information they carry also grows. Remote sensing images are characterized by large imaging areas, scattered distributions of similar objects, intricate boundary shapes, and a high density of small objects, all of which pose significant challenges for semantic segmentation tasks. To address these challenges, we propose a Remote Sensing Image Segmentation Network that Integrates Global–Local Multi-Scale Information with Deep and Shallow Features (GLDSFNet). To better handle the wide variations in object sizes and complex boundary shapes, we design a Global–Local Multi-Scale Feature Fusion Module (GLMFM) that enhances segmentation performance by fully leveraging multi-scale information and global context. Additionally, to improve the segmentation of small objects, we propose a Shallow–Deep Feature Fusion Module (SDFFM), which effectively integrates deep semantic information with shallow spatial features through mutual guidance, retaining the advantages of both. Extensive ablation and comparative experiments conducted on two public remote sensing datasets, ISPRS Vaihingen and Potsdam, demonstrate that our proposed GLDSFNet outperforms state-of-the-art methods. |
| format | Article |
| id | doaj-art-9c5359e9ef8f4feb891b6ed2e1a06a0e |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-9c5359e9ef8f4feb891b6ed2e1a06a0e2025-08-20T03:11:20ZengMDPI AGRemote Sensing2072-42922025-05-011711188010.3390/rs17111880Remote Sensing Image Segmentation Network That Integrates Global–Local Multi-Scale Information with Deep and Shallow FeaturesNan Chen0Ruiqi Yang1Yili Zhao2Qinling Dai3Leiguang Wang4School of Landscape Architecture, Southwest Forestry University, Kunming 650224, ChinaSchool of Geography, Yunnan Normal University, Kunming 650224, ChinaCollege of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming 650224, ChinaArt and Design College Engineering, Southwest Forestry University, Kunming 650224, ChinaSchool of Landscape Architecture, Southwest Forestry University, Kunming 650224, ChinaAs the spatial resolution of remote sensing images continues to increase, the complexity of the information they carry also grows. Remote sensing images are characterized by large imaging areas, scattered distributions of similar objects, intricate boundary shapes, and a high density of small objects, all of which pose significant challenges for semantic segmentation tasks. To address these challenges, we propose a Remote Sensing Image Segmentation Network that Integrates Global–Local Multi-Scale Information with Deep and Shallow Features (GLDSFNet). To better handle the wide variations in object sizes and complex boundary shapes, we design a Global–Local Multi-Scale Feature Fusion Module (GLMFM) that enhances segmentation performance by fully leveraging multi-scale information and global context. Additionally, to improve the segmentation of small objects, we propose a Shallow–Deep Feature Fusion Module (SDFFM), which effectively integrates deep semantic information with shallow spatial features through mutual guidance, retaining the advantages of both. Extensive ablation and comparative experiments conducted on two public remote sensing datasets, ISPRS Vaihingen and Potsdam, demonstrate that our proposed GLDSFNet outperforms state-of-the-art methods.https://www.mdpi.com/2072-4292/17/11/1880deformable convolutionfeature fusionmultiscale featureremote-sensing imagesemantic segmentation |
| spellingShingle | Nan Chen Ruiqi Yang Yili Zhao Qinling Dai Leiguang Wang Remote Sensing Image Segmentation Network That Integrates Global–Local Multi-Scale Information with Deep and Shallow Features Remote Sensing deformable convolution feature fusion multiscale feature remote-sensing image semantic segmentation |
| title | Remote Sensing Image Segmentation Network That Integrates Global–Local Multi-Scale Information with Deep and Shallow Features |
| title_full | Remote Sensing Image Segmentation Network That Integrates Global–Local Multi-Scale Information with Deep and Shallow Features |
| title_fullStr | Remote Sensing Image Segmentation Network That Integrates Global–Local Multi-Scale Information with Deep and Shallow Features |
| title_full_unstemmed | Remote Sensing Image Segmentation Network That Integrates Global–Local Multi-Scale Information with Deep and Shallow Features |
| title_short | Remote Sensing Image Segmentation Network That Integrates Global–Local Multi-Scale Information with Deep and Shallow Features |
| title_sort | remote sensing image segmentation network that integrates global local multi scale information with deep and shallow features |
| topic | deformable convolution feature fusion multiscale feature remote-sensing image semantic segmentation |
| url | https://www.mdpi.com/2072-4292/17/11/1880 |
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