MSHRNet: a multi-scale high-resolution network for land cover classification from high spatial resolution remote sensing images
Land cover classification is vital for land resource management. However, challenges such as feature similarity among ground objects, blurred boundaries, and indistinct small objects persist. To address these challenges, we propose the Multi-Scale High-Resolution Network (MSHRNet) for classifying gr...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2509090 |
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| _version_ | 1849224383063654400 |
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| author | Fang Chen Zhihui Ou Congrong Li Lei Wang Bo Yu |
| author_facet | Fang Chen Zhihui Ou Congrong Li Lei Wang Bo Yu |
| author_sort | Fang Chen |
| collection | DOAJ |
| description | Land cover classification is vital for land resource management. However, challenges such as feature similarity among ground objects, blurred boundaries, and indistinct small objects persist. To address these challenges, we propose the Multi-Scale High-Resolution Network (MSHRNet) for classifying ground objects from high-resolution remote sensing images. MSHRNet is an encoder-decoder network that incorporates an attentional boundary refinement branch in the decoder to sharpen object boundaries. It features a multi-scale feature interaction module that integrates feature maps across different resolutions in the encoder and enhances the importance of these fused features using a coordinate attention module. Additionally, we introduce a Laplacian operator-based boundary loss function (LBLoss) to further improve segmentation performance. Evaluated on the GID and Huawei Ascend Cup AI + Remote Sensing Image Competition datasets, MSHRNet demonstrates robustness with a mean Intersection over Union (mIoU) of 82.45% and 72.26%, respectively, and surpasses nine recently published models by at least 1.52% and 1.01% mIoU. Moreover, when tested on the LoveDA dataset without additional training, MSHRNet exhibited strong transferability, achieving an mIoU of 18.53% and surpassing the second-best model by 2.33%. This framework represents a significant advancement in land cover classification, addressing challenges of high-resolution imagery and exhibiting generalization across diverse datasets. |
| format | Article |
| id | doaj-art-964d81d2e57547d3a26173595e1d8eec |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-964d81d2e57547d3a26173595e1d8eec2025-08-25T11:24:34ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2509090MSHRNet: a multi-scale high-resolution network for land cover classification from high spatial resolution remote sensing imagesFang Chen0Zhihui Ou1Congrong Li2Lei Wang3Bo Yu4School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin People’s Republic of ChinaSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin People’s Republic of ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing, People’s Republic of ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing, People’s Republic of ChinaLand cover classification is vital for land resource management. However, challenges such as feature similarity among ground objects, blurred boundaries, and indistinct small objects persist. To address these challenges, we propose the Multi-Scale High-Resolution Network (MSHRNet) for classifying ground objects from high-resolution remote sensing images. MSHRNet is an encoder-decoder network that incorporates an attentional boundary refinement branch in the decoder to sharpen object boundaries. It features a multi-scale feature interaction module that integrates feature maps across different resolutions in the encoder and enhances the importance of these fused features using a coordinate attention module. Additionally, we introduce a Laplacian operator-based boundary loss function (LBLoss) to further improve segmentation performance. Evaluated on the GID and Huawei Ascend Cup AI + Remote Sensing Image Competition datasets, MSHRNet demonstrates robustness with a mean Intersection over Union (mIoU) of 82.45% and 72.26%, respectively, and surpasses nine recently published models by at least 1.52% and 1.01% mIoU. Moreover, when tested on the LoveDA dataset without additional training, MSHRNet exhibited strong transferability, achieving an mIoU of 18.53% and surpassing the second-best model by 2.33%. This framework represents a significant advancement in land cover classification, addressing challenges of high-resolution imagery and exhibiting generalization across diverse datasets.https://www.tandfonline.com/doi/10.1080/17538947.2025.2509090Land cover classificationSemantic segmentationHigh spatial resolutionTransformer networksBoundary refinement |
| spellingShingle | Fang Chen Zhihui Ou Congrong Li Lei Wang Bo Yu MSHRNet: a multi-scale high-resolution network for land cover classification from high spatial resolution remote sensing images International Journal of Digital Earth Land cover classification Semantic segmentation High spatial resolution Transformer networks Boundary refinement |
| title | MSHRNet: a multi-scale high-resolution network for land cover classification from high spatial resolution remote sensing images |
| title_full | MSHRNet: a multi-scale high-resolution network for land cover classification from high spatial resolution remote sensing images |
| title_fullStr | MSHRNet: a multi-scale high-resolution network for land cover classification from high spatial resolution remote sensing images |
| title_full_unstemmed | MSHRNet: a multi-scale high-resolution network for land cover classification from high spatial resolution remote sensing images |
| title_short | MSHRNet: a multi-scale high-resolution network for land cover classification from high spatial resolution remote sensing images |
| title_sort | mshrnet a multi scale high resolution network for land cover classification from high spatial resolution remote sensing images |
| topic | Land cover classification Semantic segmentation High spatial resolution Transformer networks Boundary refinement |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2509090 |
| work_keys_str_mv | AT fangchen mshrnetamultiscalehighresolutionnetworkforlandcoverclassificationfromhighspatialresolutionremotesensingimages AT zhihuiou mshrnetamultiscalehighresolutionnetworkforlandcoverclassificationfromhighspatialresolutionremotesensingimages AT congrongli mshrnetamultiscalehighresolutionnetworkforlandcoverclassificationfromhighspatialresolutionremotesensingimages AT leiwang mshrnetamultiscalehighresolutionnetworkforlandcoverclassificationfromhighspatialresolutionremotesensingimages AT boyu mshrnetamultiscalehighresolutionnetworkforlandcoverclassificationfromhighspatialresolutionremotesensingimages |