{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:15:10Z","timestamp":1774120510729,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["42201443"],"award-info":[{"award-number":["42201443"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning has achieved great success in remote sensing image change detection (CD). However, most methods focus only on the changed regions of images and cannot accurately identify their detailed semantic categories. In addition, most CD methods using convolutional neural networks (CNN) have difficulty capturing sufficient global information from images. To address the above issues, we propose a novel symmetric multi-task network (SMNet) that integrates global and local information for semantic change detection (SCD) in this paper. Specifically, we employ a hybrid unit consisting of pre-activated residual blocks (PR) and transformation blocks (TB) to construct the (PRTB) backbone, which obtains more abundant semantic features with local and global information from bi-temporal images. To accurately capture fine-grained changes, the multi-content fusion module (MCFM) is introduced, which effectively enhances change features by distinguishing foreground and background information in complex scenes. In the meantime, the multi-task prediction branches are adopted, and the multi-task loss function is used to jointly supervise model training to improve the performance of the network. Extensive experimental results on the challenging SECOND and Landsat-SCD datasets, demonstrate that our SMNet obtains 71.95% and 85.65% at mean Intersection over Union (mIoU), respectively. In addition, the proposed SMNet achieves 20.29% and 51.14% at Separated Kappa coefficient (Sek) on the SECOND and Landsat-SCD datasets, respectively. All of the above proves the effectiveness and superiority of the proposed method.<\/jats:p>","DOI":"10.3390\/rs15040949","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T05:51:06Z","timestamp":1676008266000},"page":"949","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["SMNet: Symmetric Multi-Task Network for Semantic Change Detection in Remote Sensing Images Based on CNN and Transformer"],"prefix":"10.3390","volume":"15","author":[{"given":"Yiting","family":"Niu","sequence":"first","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Haitao","family":"Guo","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Jun","family":"Lu","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0653-8373","authenticated-orcid":false,"given":"Lei","family":"Ding","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5858-7671","authenticated-orcid":false,"given":"Donghang","family":"Yu","sequence":"additional","affiliation":[{"name":"Naval Research Institute, Beijing 100070, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1038\/s41586-018-0411-9","article-title":"Global land change from 1982 to 2016","volume":"560","author":"Song","year":"2018","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.rse.2015.12.042","article-title":"Mapping sub-pixel urban expansion in China using Modis and DMSP\/OLS nighttime lights","volume":"175","author":"Huang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.rse.2017.04.021","article-title":"A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011","volume":"195","author":"Jin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","article-title":"Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote-sensing imagery","volume":"214","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2018.11.014","article-title":"Joint deep learning for land cover and land use classification","volume":"221","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2020.08.004","article-title":"Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote-sensing image classification at high spatial resolution","volume":"168","author":"Martins","year":"2020","journal-title":"ISPRS J. Photogramm."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Huang, W., Zhao, Z.B., Sun, L., and Ju, M. (2022). Dual-branch attention-assisted CNN for hyperspectral image classification. Remote Sens., 14.","DOI":"10.3390\/rs14236158"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, J., Gong, Z., Liu, X., Guo, H., Yu, D., and Ding, L. (2022). Object detection based on adaptive feature-aware method in optical remote sensing images. Remote Sens., 14.","DOI":"10.3390\/rs14153616"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dong, X., Qin, Y., Gao, Y., Fu, R., Liu, S., and Ye, Y. (2022). Attention-based multi-level feature fusion for object detection in remote sensing images. Remote Sens., 14.","DOI":"10.3390\/rs14153735"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"70947","DOI":"10.1109\/ACCESS.2022.3182370","article-title":"Enhanced lightweight end-to-end semantic segmentation for high-resolution remote sensing images","volume":"10","author":"Dong","year":"2022","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"118537","DOI":"10.1016\/j.eswa.2022.118537","article-title":"CSRNet: Cascaded selective resolution network for real-time semantic segmentation","volume":"211","author":"Xiong","year":"2021","journal-title":"Expert Sys. Applic."},{"key":"ref_12","unstructured":"Daudt, R.C., Le Saux, B.L., and Boulch, A. (2018, January 7\u201310). Fully convolutional Siamese networks for change detection. Proceedings of the 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"UNet++: Redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1109\/JSTARS.2020.2974276","article-title":"Deep Depthwise separable convolutional network for change detection in optical aerial images","volume":"13","author":"Liu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Fu, L., Li, Y., and Zhang, Y. (2021). HDFNet: Hierarchical dynamic fusion network for change detection in optical aerial images. Remote Sens., 13.","DOI":"10.3390\/rs13081440"},{"key":"ref_16","first-page":"1","article-title":"SNUNet-CD: A densely connected Siamese network for change detection of VHR images","volume":"19","author":"Fang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5598","DOI":"10.3390\/rs14215598","article-title":"IRA-MRSNet: A network model for change detection in high-resolution remote sensing images","volume":"14","author":"Ling","year":"2022","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1109\/JSTARS.2020.3037893","article-title":"DASNet: Dual attentive fully convolutional Siamese networks for change detection in high-resolution satellite images","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7296","DOI":"10.1109\/TGRS.2020.3033009","article-title":"Optical remote-sensing image change detection based on attention mechanism and image difference","volume":"59","author":"Peng","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","unstructured":"Guo, E.Q., Fu, X.S., Zhu, J.W., Deng, M., Liu, Y., Zhu, Q., and Li, H.F. (2018). Learning to measure change: Fully convolutional Siamese metric networks for scene change detection. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.isprsjprs.2021.12.005","article-title":"Land-use\/land-cover change detection based on a Siamese global learning framework for high spatial resolution remote-sensing imagery","volume":"184","author":"Zhu","year":"2022","journal-title":"ISPRS J. Photogramm."},{"key":"ref_22","unstructured":"Gao, Y., Zhou, M., and Metaxas, D.N. (October, January 27). UTNet: A hybrid transformer architecture for medical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, France."},{"key":"ref_23","first-page":"1","article-title":"Multi-content complementation network for salient object detection in optical remote-sensing images","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.isprsjprs.2022.02.021","article-title":"FCCDN: Feature constraint network for VHR image change detection","volume":"187","author":"Chen","year":"2022","journal-title":"ISPRS J. Photogramm."},{"key":"ref_25","first-page":"1","article-title":"Spatial-spectral attention network guided with change magnitude image for land cover change detection using remote-sensing images","volume":"60","author":"Lv","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7308","DOI":"10.1109\/JSTARS.2022.3200997","article-title":"Local and global feature learning with kernel scale-adaptive attention network for VHR remote sensing change detection","volume":"15","author":"Lei","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","first-page":"1","article-title":"BASNet: A boundary-aware Siamese network for accurate remote-sensing change detection","volume":"19","author":"Wei","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","first-page":"1","article-title":"Super-resolution-based change detection network with stacked attention module for images with different resolutions","volume":"60","author":"Liu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5891","DOI":"10.1109\/TGRS.2020.3011913","article-title":"SemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images","volume":"59","author":"Peng","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tsutsui, S., Hirakawa, T., Yamashita, T., and Fujiyoshi, H. (2021, January 19\u201322). Semantic segmentation and change detection by multi-task U-net. Proceedings of the IEEE International Conference on Image Processing, Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506560"},{"key":"ref_31","first-page":"102465","article-title":"SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery","volume":"103","author":"Peng","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1109\/LGRS.2020.2988032","article-title":"Building change detection for remote sensing images using a dual-task constrained deep Siamese convolutional network model","volume":"18","author":"Liu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1109\/TGRS.2018.2863224","article-title":"Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery","volume":"57","author":"Mou","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7651","DOI":"10.1109\/TGRS.2021.3055584","article-title":"A deep multitask learning framework coupling semantic segmentation and fully convolutional LSTM networks for urban change detection","volume":"59","author":"Papadomanolaki","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102783","DOI":"10.1016\/j.cviu.2019.07.003","article-title":"Multitask learning for large-scale semantic change detection","volume":"187","author":"Daudt","year":"2019","journal-title":"Comput. Vis. Image Understand."},{"key":"ref_36","first-page":"1","article-title":"Asymmetric Siamese networks for semantic change detection in aerial images","volume":"60","author":"Yang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.isprsjprs.2021.10.015","article-title":"ChangeMask: Deep multitask encoder-transformer-decoder architecture for semantic change detection","volume":"183","author":"Zheng","year":"2022","journal-title":"ISPRS J. Photogramm."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K.M., Zhang, X.Y., Ren, S.Q., and Sun, J. (1995). Identity mappings in deep residual networks, Computer Vision\u2014ECCV 2016. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1080\/17538947.2022.2111470","article-title":"A transformer-based Siamese network and an open-optical dataset for semantic-change detection of remote sensing images","volume":"15","author":"Yuan","year":"2022","journal-title":"Int. J. Digit. Earth"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.isprsjprs.2020.06.003","article-title":"A deeply supervised image fusion network for change detection in high-resolution bitemporal remote-sensing images","volume":"166","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm."},{"key":"ref_41","first-page":"1","article-title":"Bi-temporal semantic reasoning for the semantic change detection in HR remote sensing images","volume":"60","author":"Ding","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","first-page":"1","article-title":"Remote sensing image change detection with transformers","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"updated-by":[{"DOI":"10.3390\/rs15122994","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000}}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/949\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T14:02:07Z","timestamp":1754229727000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/949"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,9]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15040949"],"URL":"https:\/\/doi.org\/10.3390\/rs15040949","relation":{"correction":[{"id-type":"doi","id":"10.3390\/rs15122994","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,9]]}}}