{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T15:46:26Z","timestamp":1774021586890,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T00:00:00Z","timestamp":1707350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Aerospace Information Research Institute","award":["Y9B0640HM3"],"award-info":[{"award-number":["Y9B0640HM3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Street trees are of great importance to urban green spaces. Quick and accurate segmentation of street trees from high-resolution remote sensing images is of great significance in urban green space management. However, traditional segmentation methods can easily miss some targets because of the different sizes of street trees. To solve this problem, we propose the Double-Branch Multi-Scale Contextual Network (DB-MSC Net), which has two branches and a Multi-Scale Contextual (MSC) block in the encoder. The MSC block combines parallel dilated convolutional layers and transformer blocks to enhance the network\u2019s multi-scale feature extraction ability. A channel attention mechanism (CAM) is added to the decoder to assign weights to features from RGB images and the normalized difference vegetation index (NDVI). We proposed a benchmark dataset to test the improvement of our network. Experimental research showed that the DB-MSC Net demonstrated good performance compared with typical methods like Unet, HRnet, SETR and recent methods. The overall accuracy (OA) was improved by at least 0.16% and the mean intersection over union was improved by at least 1.13%. The model\u2019s segmentation accuracy meets the requirements of urban green space management.<\/jats:p>","DOI":"10.3390\/s24041110","type":"journal-article","created":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T09:00:02Z","timestamp":1707382802000},"page":"1110","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Double-Branch Multi-Scale Contextual Network: A Model for Multi-Scale Street Tree Segmentation in High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1210-4119","authenticated-orcid":false,"given":"Hongyang","family":"Zhang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Liu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wu, Y., Sun, Q., Hu, C., Liu, H., Chen, C., and Xiao, P. (2023). Tree failure assessment of london plane (Platanus \u00d7 acerifolia (aiton) willd.) street trees in nanjing city. Forests, 14.","DOI":"10.3390\/f14091696"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"233","DOI":"10.5194\/isprs-annals-IV-5-233-2018","article-title":"Generating GIS database of street trees using mobile lidar data","volume":"IV-5","author":"Yadav","year":"2018","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"126946","DOI":"10.1016\/j.ufug.2020.126946","article-title":"Remote sensing of urban green spaces: A review","volume":"57","author":"Shahtahmassebi","year":"2021","journal-title":"Urban For. Urban Green."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2017.11.008","article-title":"From Google Maps to a fine-grained catalog of street trees","volume":"135","author":"Branson","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1007\/s11252-012-0255-2","article-title":"Investigations of the urban street tree forest of Mendoza, Argentina","volume":"16","author":"Breuste","year":"2013","journal-title":"Urban Ecosyst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, X., Boutat, D., and Liu, D. (2023). Applications of fractional operator in image processing and stability of control systems. Fractal Fract., 7.","DOI":"10.3390\/fractalfract7050359"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hong, Z.H., Xu, S., Wang, J., and Xiao, P.F. (2009, January 20\u201322). Extraction of urban street trees from high resolution remote sensing image. Proceedings of the 2009 Joint Urban Remote Sensing Event, Shanghai, China.","DOI":"10.1109\/URS.2009.5137724"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, H.H., Xiao, P.F., and Feng, X.Z. (2013, January 26). Edge detection of street trees in high-resolution remote sensing images using spectrum features. Proceedings of the MIPPR 2013: Automatic Target Recognition and Navigation, Wuhan, China.","DOI":"10.1117\/12.2031224"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, J.X., Yang, T., and Chai, T. (2022). Neural network control of underactuated surface vehicles with prescribed trajectory tracking performance. IEEE Trans. Neural Netw. Learn. Syst., 1\u201314.","DOI":"10.1109\/TNNLS.2022.3223666"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2017). Fully convolutional networks for semantic segmentation. arXiv.","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"ref_12","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 International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_13","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":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 15\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","article-title":"Review on convolutional neural networks (CNN) in vegetation remote sensing","volume":"173","author":"Kattenborn","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107511","DOI":"10.1016\/j.compag.2022.107511","article-title":"Modified U-Net for plant diseased leaf image segmentation","volume":"204","author":"Zhang","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TCI.2022.3151472","article-title":"Injected infrared and visible image fusion via L_1 decomposition model and guided filtering","volume":"8","author":"Yan","year":"2022","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_19","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv."},{"key":"ref_20","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_21","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018). Computer Vision\u2014ECCV 2018, Proceedings of the 15th European Conference, Munich, Germany, 8\u201314 September 2018, Springer."},{"key":"ref_22","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 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_23","unstructured":"Qin, Y., Kamnitsas, K., Ancha, S., Nanavati, J., Cottrell, G., Criminisi, A., and Nori, A. (2018). Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2018, Proceedings of the 21st International Conference, Granada, Spain, 16\u201320 September 2018, Springer."},{"key":"ref_24","unstructured":"Gu, F., Burlutskiy, N., Andersson, M., and Wil\u00e9n, L.K. (2018). Computational Pathology and Ophthalmic Medical Image Analysis, Proceedings of the First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, 16\u201320 September 2018, Springer."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tokunaga, H., Teramoto, Y., Yoshizawa, A., and Bise, R. (2019, January 15\u201320). Adaptive Weighting Multi-Field-Of-View CNN for Semantic Segmentation in Pathology. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01288"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xiao, H., Li, L., Liu, Q., Zhu, X., and Zhang, Q. (2023). Transformers in medical image segmentation: A review. Biomed. Signal Process. Control, 84.","DOI":"10.1016\/j.bspc.2023.104791"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., and Torr, P.H.S. (2021, January 20\u201325). Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ye, Z., Wei, J., Lin, Y., Guo, Q., Zhang, J., Zhang, H., Deng, H., and Yang, K. (2022). Extraction of olive crown based on UAV visible images and the U2-Net deep learning model. Remote Sens., 14.","DOI":"10.3390\/rs14061523"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhou, J., Wang, H., Tan, T., Cui, M., Huang, Z., Wang, P., and Zhang, L. (2022). Multi-species individual tree segmentation and identification based on improved mask R-CNN and UAV imagery in mixed forests. Remote Sens., 14.","DOI":"10.3390\/rs14040874"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sch\u00fcrholz, D., Castellanos-Galindo, G.A., Casella, E., Mej\u00eda-Renter\u00eda, J.C., and Chennu, A. (2023). Seeing the forest for the trees: Mapping cover and counting trees from aerial images of a mangrove forest using artificial intelligence. Remote Sens., 15.","DOI":"10.3390\/rs15133334"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lv, L., Li, X., Mao, F., Zhou, L., Xuan, J., Zhao, Y., Yu, J., Song, M., Huang, L., and Du, H. (2023). A deep learning network for individual tree segmentation in UAV images with a coupled CSPNet and attention mechanism. Remote Sens., 15.","DOI":"10.3390\/rs15184420"},{"key":"ref_33","first-page":"4400911","article-title":"A domain adaptation method for land use classification based on improved HR-Net","volume":"61","author":"Zheng","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, K.-H., and Lin, B.-Y. (2023). MSCSA-Net: Multi-scale channel spatial attention network for semantic segmentation of remote sensing images. Appl. Sci., 13.","DOI":"10.3390\/app13179491"},{"key":"ref_35","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. Computer Vision\u2014ECCV 2018, Proceedings of the 15th European Conference, Munich, Germany, 8\u201314 September 2018, Springer."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1110\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:57:07Z","timestamp":1760104627000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,8]]},"references-count":35,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24041110"],"URL":"https:\/\/doi.org\/10.3390\/s24041110","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,8]]}}}