{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T21:11:02Z","timestamp":1793049062588,"version":"4.5.0"},"reference-count":51,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Semantic segmentation of very-high-resolution remote sensing imagery benefits from combining RGB appearance with Digital Surface Model (DSM) height information, especially in urban scenes where spectrally similar objects often differ in elevation. On the ISPRS Vaihingen and Potsdam benchmarks, BATFNet achieves mIoU scores of 84.06% and 85.31%, respectively, outperforming representative RGB\u2013DSM fusion baselines on most land-cover categories. BATFNet is a supervised boundary-aware Transformer fusion network that uses DSM-derived edge priors to guide bidirectional cross-modal attention and decoder refinement. With a dual-branch ResNet-50 backbone for modality-specific feature extraction, the proposed framework effectively integrates RGB and DSM information while recovering fine spatial details. These results show that exploiting DSM-derived structural cues improves boundary delineation and reduces confusion among spectrally similar urban classes.<\/jats:p>","DOI":"10.3390\/s26103205","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T08:55:28Z","timestamp":1779180928000},"page":"3205","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["BATFNet: Boundary-Aware Transformer Fusion Network for RGB-DSM Semantic Segmentation of Remote Sensing Images"],"prefix":"10.3390","volume":"26","author":[{"given":"Yilin","family":"Tong","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Wuhan Vocational College of Software and Engineering, Wuhan 430205, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4594-3787","authenticated-orcid":false,"given":"Meng","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aberystwyth University, Penglais, Aberystwyth SY23 3DB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hubei Institute of Measurement and Testing Technology, Wuhan 430223, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Wuhan Vocational College of Software and Engineering, Wuhan 430205, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Wuhan Vocational College of Software and Engineering, Wuhan 430205, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuelin","family":"He","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aberystwyth University, Penglais, Aberystwyth SY23 3DB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aberystwyth University, Penglais, Aberystwyth SY23 3DB, UK"},{"name":"Department of Computer Science, Peking University, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4758-4739","authenticated-orcid":false,"given":"Edore","family":"Akpokodje","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aberystwyth University, Penglais, Aberystwyth SY23 3DB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Wuhan Vocational College of Software and Engineering, Wuhan 430205, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"114417","DOI":"10.1016\/j.eswa.2020.114417","article-title":"A review of deep learning methods for semantic segmentation of remote sensing imagery","volume":"169","author":"Yuan","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_2","first-page":"5607713","article-title":"Multiattention network for semantic segmentation of fine-resolution remote sensing images","volume":"60","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"106921","DOI":"10.1016\/j.knosys.2021.106921","article-title":"Deep multi-level fusion network for multi-source image pixel-wise classification","volume":"221","author":"Liu","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_5","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":"Wu","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2017.11.011","article-title":"Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks","volume":"140","author":"Audebert","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","unstructured":"Sherrah, J. (2016). Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015). Fully convolutional networks for semantic segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tang, M., He, Y., Aslam, M., Akpokodje, E., and Jilani, S.F. (2025). Enhanced U-Net++ for improved semantic segmentation in landslide detection. Sensors, 25.","DOI":"10.3390\/s25092670"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017). Pyramid scene parsing network. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"120286","DOI":"10.1016\/j.ins.2024.120286","article-title":"Outlier detection method based on high-density iteration","volume":"662","author":"Zhou","year":"2024","journal-title":"Inf. Sci."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Dou, Z., Akpokodje, E., He, Y., Liu, Y., Ni, Z., Xu, C., Aslam, M., and Tang, M. (2026). DINOv3-Driven Semantic Segmentation for Landslide Mapping in Mountainous Regions. Sensors, 26.","DOI":"10.3390\/s26020406"},{"key":"ref_18","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 International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_19","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"Volume 34","author":"Xie","year":"2021","journal-title":"NIPS\u201921: Proceedings of the 35th International Conference on Neural Information Processing Systems"},{"key":"ref_20","unstructured":"Gu, A., and Dao, T. (2024). Mamba: Linear-time sequence modeling with selective state spaces. arXiv."},{"key":"ref_21","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_22","unstructured":"Liu, M., Dan, J., Lu, Z., Yu, Y., Li, Y., and Li, X. (2024). CM-UNet: Hybrid CNN-Mamba UNet for remote sensing image semantic segmentation. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104042","DOI":"10.1016\/j.imavis.2020.104042","article-title":"Deep multimodal fusion for semantic image segmentation: A survey","volume":"105","author":"Zhang","year":"2021","journal-title":"Image Vis. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/LGRS.2018.2795531","article-title":"Fully convolutional networks for semantic segmentation of very high resolution remotely sensed images combined with DSM","volume":"15","author":"Sun","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hazirbas, C., Ma, L., Domokos, C., and Cremers, D. (2016). Fusenet: Incorporating depth into semantic segmentation via fusion-based cnn architecture. Proceedings of the Asian Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-54181-5_14"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018). Squeeze-and-excitation networks. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_28","first-page":"104083","article-title":"A transformer-based multi-modal fusion network for semantic segmentation of high-resolution remote sensing imagery","volume":"133","author":"Liu","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111579","DOI":"10.1016\/j.patcog.2025.111579","article-title":"LMFNet: Lightweight Multimodal Fusion Network for high-resolution remote sensing image segmentation","volume":"164","author":"Wang","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhi, Y., Wang, Y., Zhang, F., Ma, M., and Mei, S. (2025). MSFFNet: Multimodal Spatial\u2013Frequency Fusion Network for RGB-DSM Remote Sensing Image Segmentation. Remote Sens., 17.","DOI":"10.3390\/rs17223745"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data","volume":"162","author":"Diakogiannis","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"123616","DOI":"10.1016\/j.eswa.2024.123616","article-title":"Semantic segmentation of remote sensing images based on multiscale features and global information modeling","volume":"249","author":"Gao","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_34","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_35","first-page":"1","article-title":"SFFNet: A wavelet-based spatial and frequency domain fusion network for remote sensing segmentation","volume":"62","author":"Yang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention Is All You Need, Curran Associates Inc."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"2004612","DOI":"10.1109\/TGRS.2023.3314641","article-title":"CMTFNet: CNN and multiscale transformer fusion network for remote-sensing image semantic segmentation","volume":"61","author":"Wu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","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_40","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_41","first-page":"5610713","article-title":"LSRFormer: Efficient transformer supply convolutional neural networks with global information for aerial image segmentation","volume":"62","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"11050","DOI":"10.1109\/JSTARS.2024.3407972","article-title":"Mvafg: Multiview fusion and advanced feature guidance change detection network for remote sensing images","volume":"17","author":"Zhang","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zheng, J.Q., Zhang, Y., Cui, G., and Li, L. (2024). Mamba-unet: Unet-like pure visual mamba for medical image segmentation. arXiv.","DOI":"10.2139\/ssrn.5097998"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Liu, J., Yang, H., Zhou, H.Y., Xi, Y., Yu, L., Li, C., Liang, Y., Shi, G., Yu, Y., and Zhang, S. (2024). Swin-umamba: Mamba-based unet with imagenet-based pretraining. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-031-72114-4_59"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Paisitkriangkrai, S., Sherrah, J., Janney, P., and Van-Den Hengel, A. (2015). Effective semantic pixel labelling with convolutional networks and conditional random fields. 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE.","DOI":"10.1109\/CVPRW.2015.7301381"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, X., Lin, K.Y., Wang, J., Wu, W., Qian, C., Li, H., and Zeng, G. (2020). Bi-directional cross-modality feature propagation with separation-and-aggregation gate for RGB-D semantic segmentation. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-58621-8_33"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"14679","DOI":"10.1109\/TITS.2023.3300537","article-title":"CMX: Cross-modal fusion for RGB-X semantic segmentation with transformers","volume":"24","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, X., Cao, L., Huang, W., Sun, F., and Wang, Y. (2022). Multimodal token fusion for vision transformers. 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52688.2022.01187"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.isprsjprs.2013.10.004","article-title":"Results of the ISPRS benchmark on urban object detection and 3D building reconstruction","volume":"93","author":"Rottensteiner","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_51","unstructured":"Loshchilov, I., and Hutter, F. (2017). Decoupled weight decay regularization. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/10\/3205\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T08:57:58Z","timestamp":1779181078000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/10\/3205"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,19]]},"references-count":51,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["s26103205"],"URL":"https:\/\/doi.org\/10.3390\/s26103205","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,19]]}}}