{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T21:02:39Z","timestamp":1784840559225,"version":"3.55.0"},"reference-count":47,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:00:00Z","timestamp":1769040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376066"],"award-info":[{"award-number":["62376066"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Semantic segmentation of high-resolution remote sensing imagery is a critical technology for the intelligent interpretation of sensor data, supporting automated environmental monitoring and urban sensing systems. However, processing data from dense urban scenarios remains challenging due to sensor signal occlusions (e.g., shadows) and the complexity of parsing multi-scale targets from optical sensors. Existing approaches often exhibit a trade-off between the accuracy of global semantic modeling and the precision of complex boundary recognition. While the Segment Anything Model (SAM) offers powerful zero-shot structural priors, its direct application to remote sensing is hindered by domain gaps and the lack of inherent semantic categorization. To address these limitations, we propose a dual-branch cooperative network, PriorSAM-DBNet. The main branch employs a Densely Connected Swin (DC-Swin) Transformer to capture cross-scale global features via a hierarchical shifted window attention mechanism. The auxiliary branch leverages SAM\u2019s zero-shot capability to exploit structural universality, generating object-boundary masks as robust signal priors while bypassing semantic domain shifts. Crucially, we introduce a parameter-efficient Scaled Subsampling Projection (SSP) module that employs a weight-sharing mechanism to align cross-modal features, freezing the massive SAM backbone to ensure computational viability for practical sensor applications. Furthermore, a novel Attentive Cross-Modal Fusion (ACMF) module is designed to dynamically resolve semantic ambiguities by calibrating the global context with local structural priors. Extensive experiments on the ISPRS Vaihingen, Potsdam, and LoveDA-Urban datasets demonstrate that PriorSAM-DBNet outperforms state-of-the-art approaches. By fine-tuning only 0.91 million parameters in the auxiliary branch, our method achieves mIoU scores of 82.50%, 85.59%, and 53.36%, respectively. The proposed framework offers a scalable, high-precision solution for remote sensing semantic segmentation, particularly effective for disaster emergency response where rapid feature recognition from sensor streams is paramount.<\/jats:p>","DOI":"10.3390\/s26020749","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T16:13:48Z","timestamp":1769444028000},"page":"749","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["PriorSAM-DBNet: A SAM-Prior-Enhanced Dual-Branch Network for Efficient Semantic Segmentation of High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"26","author":[{"given":"Qiwei","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Guizhou University, Guiyang 550025, China"},{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2126-7006","authenticated-orcid":false,"given":"Yisong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Guizhou University, Guiyang 550025, China"},{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quanwen","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Guizhou University, Guiyang 550025, China"},{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"He","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1109\/MGRS.2022.3145854","article-title":"Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","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":"Marmanis","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE CVPR, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the MICCAI, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the IEEE CVPR, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_7","unstructured":"Li, J., Zheng, P., and Wang, L. (2024, January 13\u201315). Lightweight Semantic Segmentation Network for Remote Sensing Urban Scenes Based on Selective Kernel. Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC), Wuhan, China."},{"key":"ref_8","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proceedings of the IEEE\/CVF ICCV, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wu, H., Huang, P., Zhang, M., Tang, W., and Yu, X. (2023). CMTFNet: CNN and multiscale transformer fusion network for remote-sensing image semantic segmentation. IEEE Trans. Geosci. Remote Sens., 61.","DOI":"10.1109\/TGRS.2023.3314641"},{"key":"ref_11","first-page":"12077","article-title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","volume":"Volume 34","author":"Xie","year":"2021","journal-title":"NeurIPS 2021"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, Z., Zhang, W., Zhang, T., Yang, Z., and Li, J. (2021). Efficient Transformer for Remote Sensing Image Segmentation. Remote Sens., 13.","DOI":"10.3390\/rs13183585"},{"key":"ref_13","first-page":"6506105","article-title":"A novel transformer based semantic segmentation scheme for fine-resolution remote sensing images","volume":"19","author":"Wang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"66931","DOI":"10.1109\/ACCESS.2025.3561325","article-title":"FML-Swin: An improved Swin Transformer Segmentor for Remote Sensing Images","volume":"13","author":"Wu","year":"2025","journal-title":"IEEE Access"},{"key":"ref_15","first-page":"1121","article-title":"Semantic Segmentation Method for Remote Sensing Urban Scenes Based on Swin-Transformer and Convolutional Neural Network","volume":"Volume 7","author":"Fan","year":"2024","journal-title":"Proceedings of the 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China, 20\u201322 September 2024"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.Y. (2023). Segment Anything. arXiv.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5610816","DOI":"10.1109\/TGRS.2025.3531930","article-title":"SAM enhanced semantic segmentation for remote sensing imagery without additional training","volume":"63","author":"Qiao","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5636916","DOI":"10.1109\/TGRS.2024.3443420","article-title":"SAM-assisted remote sensing imagery semantic segmentation with object and boundary constraints","volume":"62","author":"Ma","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, Y., Yang, X., Jiang, R., and Zhang, L. (2025). RSAM-Seg: A SAM-Based Model with Prior Knowledge Integration for Remote Sensing Image Semantic Segmentation. Remote Sens., 17.","DOI":"10.3390\/rs17040590"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"115018","DOI":"10.1109\/ACCESS.2025.3583458","article-title":"SAM2Former: Segment Anything Model 2 Assisting UNet-Like Transformer for Remote Sensing Image Semantic Segmentation","volume":"13","author":"Li","year":"2025","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5405015","DOI":"10.1109\/TGRS.2025.3585238","article-title":"A Unified Framework with Multimodal Fine-tuning for Remote Sensing Semantic Segmentation","volume":"63","author":"Ma","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","unstructured":"Zhang, Y., Li, W., Zhang, M., Han, J., Tao, R., and Liang, S. (2025). SpectralX: Parameter-efficient Domain Generalization for Spectral Remote Sensing Foundation Models. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, K., Liu, C., Chen, H., Zhang, H., Li, W., Zou, Z., and Shi, Z. (2023). RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model. arXiv.","DOI":"10.1109\/TGRS.2024.3356074"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2737","DOI":"10.1109\/JSTARS.2024.3504409","article-title":"Tuning a SAM-Based Model with Multicognitive Visual Adapter to Remote Sensing Instance Segmentation","volume":"18","author":"Zheng","year":"2025","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4791","DOI":"10.1109\/JSTARS.2025.3532690","article-title":"ClassWise-SAM-Adapter: Parameter Efficient Fine-tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation","volume":"18","author":"Pu","year":"2025","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Du, B., Shan, L., Shao, X., Zhang, D., Wang, X., and Wu, J. (2025). Transform Dual-Branch Attention Net: Efficient Semantic Segmentation of Ultra-High-Resolution Remote Sensing Images. Remote Sens., 17.","DOI":"10.3390\/rs17030540"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, Y., and Zhu, W. (2025). DBSANet: A Dual-Branch Semantic Aggregation Network Integrating CNNs and Transformers for Landslide Detection in Remote Sensing Images. Remote Sens., 17.","DOI":"10.3390\/rs17050807"},{"key":"ref_28","unstructured":"Chen, S., Yun, L., Liu, Z., Zhu, J., Chen, J., Wang, H., and Nie, Y. (2025). LightFormer: A Lightweight Decoder for Semantic Segmentation of Remote Sensing Images. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.3390\/rs15071860","article-title":"Transformers in Remote Sensing: A Survey","volume":"15","author":"Aleissaee","year":"2023","journal-title":"Remote Sens."},{"key":"ref_30","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4408715","DOI":"10.1109\/TGRS.2022.3144165","article-title":"Swin Transformer Embedding UNet for Remote Sensing Image Semantic Segmentation","volume":"60","author":"He","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","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":"Wang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1109\/OJSP.2025.3573202","article-title":"Swin Transformer with Spatial and Local Context Augmentation for Enhanced Semantic Segmentation of Remote Sensing Images","volume":"6","author":"Ding","year":"2025","journal-title":"IEEE Open J. Signal Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2443","DOI":"10.3390\/rs17142443","article-title":"AFNE-Net: Semantic Segmentation of Remote Sensing Images via Attention-Based Feature Fusion and Neighborhood Feature Enhancement","volume":"17","author":"Li","year":"2025","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, T., Zhu, L., Ding, C., Cao, R., Zhang, S., Wang, Y., Li, Z., Sun, L., Mao, P., and Zang, Y. (2023). SAM Fails to Segment Anything?\u2013SAM-Adapter: Adapting SAM in Underperformed Scenes. arXiv.","DOI":"10.1109\/ICCVW60793.2023.00361"},{"key":"ref_36","unstructured":"Liu, Y., Zhang, Y., Wang, Y., Hou, F., Yuan, J., Tian, J., Zhang, Y., Shi, Z., Fan, J., and He, L. (2023). SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM. arXiv."},{"key":"ref_37","unstructured":"Zhao, X., Ding, W., An, Y., Du, Y., Yu, T., Li, M., Tang, M., and Wang, J. (2023). FastSAM: A Fast Segment Anything Model. arXiv."},{"key":"ref_38","first-page":"5608515","article-title":"PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images","volume":"63","author":"Liu","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s44267-025-00106-w","article-title":"SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation","volume":"4","author":"Xiong","year":"2026","journal-title":"Vis. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gong, Z., Wei, Z., Wang, D., Hu, X., Ma, X., Chen, H., Jia, Y., Deng, Y., Ji, Z., and Zhu, X. (2024). CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic Segmentation. arXiv.","DOI":"10.1109\/TPAMI.2025.3649001"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., and Brennan, S.E. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372.","DOI":"10.1136\/bmj.n71"},{"key":"ref_42","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":"Li","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhuang, J., Yang, J., Gu, L., and Duan, N. (2019). Shelfnet for fast semantic segmentation. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France, 2\u20136 October 2023, IEEE.","DOI":"10.1109\/ICCVW.2019.00113"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6011405","DOI":"10.1109\/LGRS.2024.3414293","article-title":"Rs3mamba: Visual state space model for remote sensing image semantic segmentation","volume":"21","author":"Ma","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","first-page":"5623515","article-title":"MeSAM: Multiscale enhanced segment anything model for optical remote sensing images","volume":"62","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1080\/2150704X.2025.2461331","article-title":"A SAM-based dual-branch network for remote sensing semantic segmentation","volume":"16","author":"Zhang","year":"2025","journal-title":"Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/LRA.2020.3039744","article-title":"Real-time semantic segmentation with fast attention","volume":"6","author":"Hu","year":"2020","journal-title":"IEEE Robot. Autom. Lett."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/2\/749\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T16:18:11Z","timestamp":1769444291000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/2\/749"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,22]]},"references-count":47,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["s26020749"],"URL":"https:\/\/doi.org\/10.3390\/s26020749","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,22]]}}}