{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T11:58:50Z","timestamp":1787313530207,"version":"build-2736575974"},"reference-count":57,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Guangxi Science and Technology Major Project","award":["Grant No. AA19254016"],"award-info":[{"award-number":["Grant No. AA19254016"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Deep learning has recently made remarkable progress in remote sensing image segmentation, with hybrid architectures that integrate convolutional neural networks (CNNs) and Transformers emerging as a promising solution, particularly for high-resolution imagery. However, challenges remain in complex remote sensing scenes, particularly in capturing detailed boundary structures and small-scale targets. One key limitation lies in the suboptimal cross-level feature fusion within the encoder, resulting in semantic misalignment that hinders the precise segmentation of small objects and fine structural details. Additionally, during the decoding stage, the lack of explicit boundary guidance frequently causes the loss of edge information during feature reconstruction, compromising the delineation of object contours in intricate environments. To address these issues, We propose a novel hybrid architecture named Boundary-Guided Semantic Compensation Network (BGSC-Net). Our framework integrates two key components: a Cross-Level Semantic Compensation Module (CLSCM) that dynamically fuses high-level semantics with low-level spatial details to enhance small object segmentation, and an Auxiliary Boundary Supervision Module (ABSM) that enhances structural modeling for blurry or complex boundaries through explicit boundary modeling and an auxiliary supervision strategy based on joint optimization of the edge and main segmentation branches. Experiments show that BGSC-Net achieves superior segmentation performance, with mIoU scores of 87.57% on Potsdam, 85.61% on Vaihingen, 55.05% on LoveDA, and 74.77% on UAVid. To further validate its generalization capability in specialized fine-grained segmentation tasks, we evaluated the model on our challenging self-constructed Mangrove Species Fine-grained Segmentation Dataset (MSFSD), where it achieved an mIoU of 89.58%, confirming its practical utility for precise mangrove species mapping.<\/jats:p>","DOI":"10.1371\/journal.pone.0345762","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T17:28:27Z","timestamp":1774978107000},"page":"e0345762","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["BGSC-Net: Boundary-guided semantic compensation network for remote sensing image segmentation"],"prefix":"10.1371","volume":"21","author":[{"given":"Xin","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9759-8015","authenticated-orcid":true,"given":"Qun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guan","family":"Lian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"key":"pone.0345762.ref001","first-page":"117","article-title":"Remote Sensing Image Segmentation: Methods, Approaches, and Advances","volume-title":"Remote Sensing Handbook, Volume II","author":"MD Hossain","year":"2025"},{"issue":"1","key":"pone.0345762.ref002","doi-asserted-by":"crossref","first-page":"274","DOI":"10.3390\/su16010274","article-title":"The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives","volume":"16","author":"Z Gu","year":"2023","journal-title":"Sustainability"},{"issue":"7","key":"pone.0345762.ref003","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2014.2380779","article-title":"Spatiotemporal detection and analysis of urban villages in mega city regions of China using high-resolution remotely sensed imagery","volume":"53","author":"X Huang","year":"2015","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0345762.ref004","doi-asserted-by":"crossref","first-page":"7422","DOI":"10.1109\/JSTARS.2021.3098678","article-title":"Deep-learning-based multispectral satellite image segmentation for water body detection","volume":"14","author":"K Yuan","year":"2021","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sensing"},{"key":"pone.0345762.ref005","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.isprsjprs.2017.11.009","article-title":"Classification with an edge: Improving semantic image segmentation with boundary detection","volume":"135","author":"D Marmanis","year":"2018","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"pone.0345762.ref006","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015;3431\u201340. https:\/\/doi.org\/10.1109\/cvpr.2015.7298965","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"pone.0345762.ref007","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III, 2015.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"pone.0345762.ref008","volume-title":"Pyramid scene parsing network. Proceedings of the IEEE conference on computer vision and pattern recognition","author":"H Zhao","year":"2017"},{"key":"pone.0345762.ref009","doi-asserted-by":"crossref","first-page":"7233","DOI":"10.1109\/JSTARS.2024.3375313","article-title":"CMLFormer: CNN and Multiscale Local-Context Transformer Network for Remote Sensing Images Semantic Segmentation","volume":"17","author":"H Wu","year":"2024","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sensing"},{"key":"pone.0345762.ref010","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.isprsjprs.2022.06.008","article-title":"UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery","volume":"190","author":"L Wang","year":"2022","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"issue":"17","key":"pone.0345762.ref011","doi-asserted-by":"crossref","first-page":"3278","DOI":"10.3390\/rs16173278","article-title":"SFA-Net: Semantic Feature Adjustment Network for Remote Sensing Image Segmentation","volume":"16","author":"G Hwang","year":"2024","journal-title":"Remote Sensing"},{"issue":"6","key":"pone.0345762.ref012","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.3390\/rs17061019","article-title":"GLFFNet: Global\u2013Local Feature Fusion Network for High-Resolution Remote Sensing Image Semantic Segmentation","volume":"17","author":"S Zhu","year":"2025","journal-title":"Remote Sensing"},{"issue":"7","key":"pone.0345762.ref013","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.3390\/rs15071860","article-title":"Transformers in Remote Sensing: A Survey","volume":"15","author":"AA Aleissaee","year":"2023","journal-title":"Remote Sensing"},{"key":"pone.0345762.ref014","doi-asserted-by":"crossref","first-page":"112134","DOI":"10.1016\/j.ymssp.2024.112134","article-title":"Surrogate modeling of pantograph-catenary system interactions","volume":"224","author":"Y Cheng","year":"2025","journal-title":"Mechanical Systems and Signal Processing"},{"key":"pone.0345762.ref015","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z. Swin transformer: hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF international conference on computer version; 2021.","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"23","key":"pone.0345762.ref016","doi-asserted-by":"crossref","first-page":"4494","DOI":"10.3390\/rs16234494","article-title":"Monitoring anthropogenically disturbed parcels with soil erosion dynamics change based on an improved segFormer","volume":"16","author":"Z Li","year":"2024","journal-title":"Remote Sensing"},{"key":"pone.0345762.ref017","doi-asserted-by":"crossref","unstructured":"Themyr L, Rambour C, Thome N, Collins T, Hostettler A. Full Contextual Attention for Multi-resolution Transformers in Semantic Segmentation. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV). 2023;3223\u201332. https:\/\/doi.org\/10.1109\/wacv56688.2023.00324","DOI":"10.1109\/WACV56688.2023.00324"},{"key":"pone.0345762.ref018","author":"J Chen","year":"2021"},{"key":"pone.0345762.ref019","doi-asserted-by":"crossref","first-page":"106516","DOI":"10.1016\/j.compbiomed.2022.106516","article-title":"ST-Unet: Swin Transformer boosted U-Net with Cross-Layer Feature Enhancement for medical image segmentation","volume":"153","author":"J Zhang","year":"2023","journal-title":"Comput Biol Med"},{"key":"pone.0345762.ref020","first-page":"1","article-title":"Semantic Segmentation of Remote Sensing Images by Interactive Representation Refinement and Geometric Prior-Guided Inference","volume":"62","author":"X Li","year":"2024","journal-title":"IEEE Trans Geosci Remote Sensing"},{"issue":"1","key":"pone.0345762.ref021","doi-asserted-by":"crossref","first-page":"15371","DOI":"10.1038\/s41598-025-99835-7","article-title":"Multi-scale feature fusion and feature calibration with edge information enhancement for remote sensing object detection","volume":"15","author":"L Yang","year":"2025","journal-title":"Sci Rep"},{"key":"pone.0345762.ref022","author":"J Zhou","year":"2024"},{"key":"pone.0345762.ref023","doi-asserted-by":"crossref","first-page":"4689","DOI":"10.1109\/JSTARS.2024.3361927","article-title":"CTMANet: A CNN-Transformer Hybrid Semantic Segmentation Network for Fine-Grained Airport Extraction in Complex SAR Scenes","volume":"17","author":"K Wu","year":"2024","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sensing"},{"issue":"14","key":"pone.0345762.ref024","doi-asserted-by":"crossref","first-page":"2526","DOI":"10.3390\/rs16142526","article-title":"BAFormer: A Novel Boundary-Aware Compensation UNet-like Transformer for High-Resolution Cropland Extraction","volume":"16","author":"Z Li","year":"2024","journal-title":"Remote Sensing"},{"key":"pone.0345762.ref025","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3314641","article-title":"CMTFNet: CNN and multiscale transformer fusion network for remote-sensing image semantic segmentation","volume":"61","author":"H Wu","year":"2023","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0345762.ref026","doi-asserted-by":"crossref","first-page":"2372","DOI":"10.1109\/JSTARS.2023.3347595","article-title":"Dual encoder\u2013decoder network for land cover segmentation of remote sensing image","volume":"17","author":"Z Wang","year":"2024","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sensing"},{"key":"pone.0345762.ref027","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3144165","article-title":"Swin transformer embedding unet for remote sensing image semantic segmentation","volume":"60","author":"X He","year":"2022","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0345762.ref028","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1016\/j.isprsjprs.2025.01.017","article-title":"Accurate semantic segmentation of very high-resolution remote sensing images considering feature state sequences: From benchmark datasets to urban applications","volume":"220","author":"Z Wang","year":"2025","journal-title":"ISPRS J Photogrammetry Remote Sensing"},{"issue":"3","key":"pone.0345762.ref029","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1080\/01431161.2022.2030071","article-title":"A2-FPN for semantic segmentation of fine-resolution remotely sensed images","volume":"43","author":"R Li","year":"2022","journal-title":"International J Remote Sensing"},{"key":"pone.0345762.ref030","first-page":"1","article-title":"Multistage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images","volume":"19","author":"R Li","year":"2022","journal-title":"IEEE Geosci Remote Sensing Lett"},{"key":"pone.0345762.ref031","first-page":"1","article-title":"A Synergistical Attention Model for Semantic Segmentation of Remote Sensing Images","volume":"61","author":"X Li","year":"2023","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0345762.ref032","doi-asserted-by":"crossref","unstructured":"Zhang Z, Zhang X, Peng C, Xue X, Sun J. Exfuse: Enhancing feature fusion for semantic segmentation. In: 2018.","DOI":"10.1007\/978-3-030-01249-6_17"},{"key":"pone.0345762.ref033","doi-asserted-by":"crossref","first-page":"103359","DOI":"10.1016\/j.inffus.2025.103359","article-title":"Dual-domain decoupled fusion network for semantic segmentation of remote sensing images","volume":"124","author":"X Li","year":"2025","journal-title":"Information Fusion"},{"key":"pone.0345762.ref034","article-title":"CM-UNet: Hybrid CNN-Mamba UNet for remote sensing image semantic segmentation","author":"M Liu","year":"2024"},{"key":"pone.0345762.ref035","first-page":"1","article-title":"A Euclidean Affinity-Augmented Hyperbolic Neural Network for Semantic Segmentation of Remote Sensing Images","volume":"63","author":"X Li","year":"2025","journal-title":"IEEE Trans Geosci Remote Sensing"},{"issue":"15","key":"pone.0345762.ref036","doi-asserted-by":"crossref","first-page":"24972","DOI":"10.1109\/JSEN.2024.3409756","article-title":"Boundary Enhancement-Driven Accurate Semantic Segmentation Networks for Unmanned Surface Vessels in Complex Marine Environments","volume":"24","author":"L Zhang","year":"2024","journal-title":"IEEE Sensors J"},{"issue":"12","key":"pone.0345762.ref037","doi-asserted-by":"crossref","first-page":"2076","DOI":"10.3390\/rs16122076","article-title":"DAENet: Deformable Attention Edge Network for Automatic Coastline Extraction from Satellite Imagery","volume":"16","author":"B Kang","year":"2024","journal-title":"Remote Sensing"},{"key":"pone.0345762.ref038","first-page":"1","article-title":"A Frequency Decoupling Network for Semantic Segmentation of Remote Sensing Images","volume":"63","author":"X Li","year":"2025","journal-title":"IEEE Trans Geosci Remote Sensing"},{"issue":"6","key":"pone.0345762.ref039","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.3390\/rs14061353","article-title":"Edge Guided Context Aggregation Network for Semantic Segmentation of Remote Sensing Imagery","volume":"14","author":"Z Liu","year":"2022","journal-title":"Remote Sensing"},{"key":"pone.0345762.ref040","article-title":"Research on multimodal techniques for arc detection in railway systems with limited data","author":"J Yan","year":"2025","journal-title":"Structural Health Monitoring"},{"key":"pone.0345762.ref041","author":"Z Zhang","year":"2022"},{"issue":"9","key":"pone.0345762.ref042","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.3390\/rs10091339","article-title":"ERN: Edge loss reinforced semantic segmentation network for remote sensing images","volume":"10","author":"S Liu","year":"2018","journal-title":"Remote Sensing"},{"key":"pone.0345762.ref043","first-page":"1","article-title":"Multimodal Imitation Learning for Arc Detection in Complex Railway Environments","volume":"74","author":"J Yan","year":"2025","journal-title":"IEEE Trans Instrum Meas"},{"key":"pone.0345762.ref044","unstructured":"Tan M, Le Q, editors. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning. 2019."},{"key":"pone.0345762.ref045","doi-asserted-by":"crossref","first-page":"14078","DOI":"10.1109\/ACCESS.2021.3051085","article-title":"Classification of remote sensing images using EfficientNet-B3 CNN model with attention","volume":"9","author":"H Alhichri","year":"2021","journal-title":"IEEE Access"},{"issue":"3","key":"pone.0345762.ref046","doi-asserted-by":"crossref","first-page":"1410","DOI":"10.1007\/s11263-024-02247-9","article-title":"LSKNet: a foundation lightweight backbone for remote sensing","volume":"133","author":"Y Li","year":"2024","journal-title":"Int J Comput Vis"},{"key":"pone.0345762.ref047","unstructured":"International Society for PR. Potsdam and Vaihingen datasets, Photogrammetry Remote, Sensing. 2024."},{"key":"pone.0345762.ref048","author":"J Wang","year":"2021"},{"key":"pone.0345762.ref049","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2020.05.009","article-title":"UAVid: A semantic segmentation dataset for UAV imagery","volume":"165","author":"Y Lyu","year":"2020","journal-title":"ISPRS J Photogrammetry Remote Sensing"},{"key":"pone.0345762.ref050","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.isprsjprs.2021.09.005","article-title":"ABCNet: Attentive bilateral contextual network for efficient semantic segmentation of Fine-Resolution remotely sensed imagery","volume":"181","author":"R Li","year":"2021","journal-title":"ISPRS J Photogrammetry and Remote Sensing"},{"key":"pone.0345762.ref051","first-page":"1","article-title":"Multiattention Network for Semantic Segmentation of Fine-Resolution Remote Sensing Images","volume":"60","author":"R Li","year":"2022","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0345762.ref052","first-page":"1","article-title":"SFFNet: A Wavelet-Based Spatial and Frequency Domain Fusion Network for Remote Sensing Segmentation","volume":"62","author":"Y Yang","year":"2024","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0345762.ref053","article-title":"DecoupleNet: A Lightweight Backbone Network With Efficient Feature Decoupling for Remote Sensing Visual Tasks","author":"W Lu","year":"2024","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"pone.0345762.ref054","first-page":"1","article-title":"A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images","volume":"19","author":"L Wang","year":"2022","journal-title":"IEEE Geosci Remote Sensing Lett"},{"key":"pone.0345762.ref055","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2025.3583765","article-title":"Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation","volume":"63","author":"F Gao","year":"2025","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0345762.ref056","author":"J Shi","year":"2025"},{"key":"pone.0345762.ref057","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2024.3379398","article-title":"CGGLNet: semantic segmentation network for remote sensing images based on category-guided global\u2013local feature interaction","volume":"62","author":"Y Ni","year":"2024","journal-title":"IEEE Trans Geosci Remote Sensing"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0345762","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T17:28:43Z","timestamp":1774978123000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0345762"}},"subtitle":[],"editor":[{"given":"Xiaoyong","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,3,31]]},"references-count":57,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3,31]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0345762","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,31]]}}}