{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:31:02Z","timestamp":1771612262133,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T00:00:00Z","timestamp":1716336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["42301457"],"award-info":[{"award-number":["42301457"]}]},{"name":"the National Natural Science Foundation of China","award":["42192583"],"award-info":[{"award-number":["42192583"]}]},{"name":"the National Natural Science Foundation of China","award":["42301434"],"award-info":[{"award-number":["42301434"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The challenge of detecting changes in high-resolution remote sensing imagery often stems from the difficulties in effectively extracting features and constructing appropriate change detection models considering the scale characteristics of ground objects. To solve these issues, we propose a novel UNet 3+ change detection method that considers the scale characteristics inherent in various land-cover change types. Our method includes three key steps: a multi-scale segmentation method, a class-specific UNet 3+ method, and an object-oriented change detection method based on UNet 3+. To verify the effectiveness of this method, we select two datasets for experiments and compare our proposed method with the UNet 3+ single-scale sampling method, the class-specific UNet 3+ single-scale sampling method, and the UNet 3+ multi-scale hierarchical sampling method. Our experimental results show that our proposed method has higher overall accuracy and F1, lower missed detection rate and false detection rate, and can detect more changes in ground features than other methods. To verify the scalability of this method, we compare this method with traditional change detection methods such as PCA-k-means, OCVA, a single-scale sampling method based on random forest, and a class-specific object-based method. Experimental results and accuracy indexes show that our proposed method better considers the scale characteristics of ground objects and achieves higher accuracy. Additionally, we compared our proposed method with other DLCD methods including LamboiseNet, BIT, CDNet, FCSiamConc, and FCSiamDiff. Our results show that our proposed method effectively considers edge information and has an acceptable time consumption. Our approach not only considers the full-scale characteristics of the feature extraction but also the scale characteristics of the change detection model. In addition, it considers a more practical feature extraction unit (object), making it more accurate.<\/jats:p>","DOI":"10.3390\/rs16111846","type":"journal-article","created":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T07:56:11Z","timestamp":1716364571000},"page":"1846","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Novel UNet 3+ Change Detection Method Considering Scale Uncertainty in High-Resolution Imagery"],"prefix":"10.3390","volume":"16","author":[{"given":"Ting","family":"Bai","sequence":"first","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430010, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3219-2591","authenticated-orcid":false,"given":"Qing","family":"An","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China"}]},{"given":"Shiquan","family":"Deng","sequence":"additional","affiliation":[{"name":"Wuhan Academy of Water Science, Wuhan 430010, China"}]},{"given":"Pengfei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430010, China"}]},{"given":"Yepei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430010, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2664-9479","authenticated-orcid":false,"given":"Kaimin","family":"Sun","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Geo-Information Engineering, Xi\u2019an 710054, China"},{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430010, China"}]},{"given":"Huajian","family":"Zheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou 510663, China"},{"name":"Surveying and Mapping Institute Lands and Resource Department of Guangdong Province, Guangzhou 510663, China"},{"name":"Guangdong Science and Technology Collaborative Innovation Center for Natural Resources, Guangzhou 510663, China"}]},{"given":"Zhina","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430010, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,22]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Joint spatio-temporal modeling for semantic change detection in remote sensing images","volume":"62","author":"Ding","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","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":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"789","DOI":"10.5194\/nhess-23-789-2023","article-title":"Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning","volume":"23","author":"Rao","year":"2023","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_4","first-page":"103785","article-title":"A building change detection framework with patch-pairing single-temporal supervised learning and metric guided attention mechanism","volume":"129","author":"Gao","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","first-page":"103767","article-title":"Robust change detection for remote sensing images based on temporospatial interactive attention module","volume":"128","author":"Wei","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1080\/10095020.2022.2085633","article-title":"Deep learning for change detection in remote sensing: A review","volume":"26","author":"Bai","year":"2023","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1080\/0020723042000286374","article-title":"A fast environmental change detection approach based on unsupervised multiscale texture clustering","volume":"62","author":"Ouma","year":"2005","journal-title":"Int. J. Environ. Stud."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2963","DOI":"10.1109\/TGRS.2005.857987","article-title":"A detail-preserving scale-driven approach to change detection in multitemporal SAR images","volume":"43","author":"Bovolo","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Eklund, P.W., You, J., and Deer, P. (2000, January 6). Mining remote sensing image data: An integration of fuzzy set theory and image understanding techniques for environmental change detection. Proceedings of the Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, Orlando, FL, USA.","DOI":"10.1117\/12.381741"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, X., Liu, S., Du, P., Liang, H., Xia, J., and Li, Y. (2018). Object-based change detection in urban areas from high spatial resolution images based on multiple features and ensemble learning. Remote Sens., 10.","DOI":"10.3390\/rs10020276"},{"key":"ref_11","unstructured":"Wang, P. (2007). Research on image segmentation method based on multi-scale theory. [Ph.D. Thesis, University of Science and Technology of China]."},{"key":"ref_12","unstructured":"Huang, Z. (2014). Research on Multiscale Methods in Object-Based Image Analysis. [Ph.D. Thesis, National University of Defense Technology]."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Feng, W., Sui, H., Tu, J., Huang, W., Xu, C., and Sun, K. (2018). A novel change detection approach for multi-temporal high-resolution remote sensing images based on rotation forest and coarse-to-fine uncertainty analyses. Remote Sens., 10.","DOI":"10.3390\/rs10071015"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Cao, J., Lv, Z., and Benediktsson, J.A. (2019). Spatial\u2013Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images. Remote Sens., 11.","DOI":"10.3390\/rs11161903"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3641","DOI":"10.1109\/JSTARS.2017.2693993","article-title":"Adaptive Scale Selection for Multiscale Segmentation of Satellite Images","volume":"10","author":"Zhou","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1080\/15481603.2017.1287238","article-title":"A comparison of unsupervised segmentation parameter optimization approaches using moderate-and high-resolution imagery","volume":"54","author":"Grybas","year":"2017","journal-title":"GIScience Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2658","DOI":"10.1109\/TGRS.2017.2650198","article-title":"Superpixel-based difference representation learning for change detection in multispectral remote sensing images","volume":"55","author":"Gong","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","unstructured":"Bengio, Y. (2011, January 2). Deep learning of representations for unsupervised and transfer learning. Proceedings of the ICML Workshop on Unsupervised and Transfer Learning, Washington, DC, USA."},{"key":"ref_19","unstructured":"Levien, L.M., Fischer, C., Roffers, P., Maurizi, B., Suero, J., Fischer, C., and Huang, X. (1999, January 20). A machine-learning approach to change detection using multi-scale imagery. Proceedings of the ASPRS Annual Conference, Portland, OR, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change detection techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1016\/j.rse.2007.07.023","article-title":"Use of a dark object concept and support vector machines to automate forest cover change analysis","volume":"112","author":"Huang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Lyu, H., Lu, H., and Mou, L. (2016). Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection. Remote Sens., 8.","DOI":"10.3390\/rs8060506"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1080\/2150704X.2018.1492172","article-title":"Change detection based on Faster R-CNN for high-resolution remote sensing images","volume":"9","author":"Wang","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xu, Y., Xiang, S.M., Huo, C.L., and Pan, C.H. (2013, January 27). Change Detection Based on Auto-encoder Model for VHR Images. Proceedings of the Mippr 2013: Pattern Recognition and Computer Vision, Wuhan, China.","DOI":"10.1117\/12.2031104"},{"key":"ref_27","unstructured":"El Amin, A.M., Liu, Q., and Wang, Y. (2016, January 11). Convolutional neural network features based change detection in satellite images. Proceedings of the First International Workshop on Pattern Recognition, Tokyo, Japan."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2310","DOI":"10.1109\/LGRS.2017.2762694","article-title":"Generative Adversarial Networks for Change Detection in Multispectral Imagery","volume":"14","author":"Gong","year":"2017","journal-title":"Ieee Geosci Remote S"},{"key":"ref_29","first-page":"1","article-title":"Asymmetric cross-attention hierarchical network based on CNN and transformer for bitemporal remote sensing images change detection","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","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_31","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_32","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1109\/LGRS.2018.2868880","article-title":"Objects segmentation from high-resolution aerial images using U-Net with pyramid pooling layers","volume":"16","author":"Kim","year":"2018","journal-title":"IEEE Geosci. Remote S"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018, January 20). Unet++: A nested u-net architecture for medical image segmentation. Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Granada, Spain.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Peng, D., Zhang, Y., and Guan, H. (2019). End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sens., 11.","DOI":"10.3390\/rs11111382"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.-W., and Wu, J. (2020, January 4). Unet 3+: A full-scale connected unet for medical image segmentation. Proceedings of the ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"101434","DOI":"10.1109\/ACCESS.2022.3208134","article-title":"SAUNet3+ CD: A Siamese-attentive UNet3+ for change detection in remote sensing images","volume":"10","author":"Mo","year":"2022","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/15481603.2018.1426091","article-title":"Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system","volume":"55","author":"Liu","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"112308","DOI":"10.1016\/j.rse.2021.112308","article-title":"Change detection using deep learning approach with object-based image analysis","volume":"256","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"112636","DOI":"10.1016\/j.rse.2021.112636","article-title":"Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters","volume":"265","author":"Zheng","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1109\/36.175340","article-title":"The impact of misregistration on change detection","volume":"30","author":"Townshend","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.rse.2012.06.011","article-title":"A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species","volume":"124","author":"Pu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ma, L., Li, M., Blaschke, T., Ma, X., Tiede, D., Cheng, L., Chen, Z., and Chen, D. (2016). Object-based change detection in urban areas: The effects of segmentation strategy, scale, and feature space on unsupervised methods. Remote Sens., 8.","DOI":"10.3390\/rs8090761"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"7523","DOI":"10.1080\/01431161.2018.1471542","article-title":"Multi-scale hierarchical sampling change detection using Random Forest for high-resolution satellite imagery","volume":"39","author":"Bai","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"249","DOI":"10.14358\/PERS.87.4.249","article-title":"A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery","volume":"87","author":"Bai","year":"2021","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.rse.2003.10.022","article-title":"Impacts of imagery temporal frequency on land-cover change detection monitoring","volume":"89","author":"Lunetta","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_47","unstructured":"Beitzel, S.M. (2006). On Understanding and Classifying Web Queries, Illinois Institute of Technology."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","article-title":"Unsupervised change detection in satellite images using principal component analysis and $ k $-means clustering","volume":"6","author":"Celik","year":"2009","journal-title":"IEEE Geosci. Remote S"},{"key":"ref_49","unstructured":"Sun, K., and Chen, Y. (2010, January 6\u20137). The Application of Objects Change Vector Analysis in Object-level Change Detection. Proceedings of the International Conference on Computational Intelligence and Industrial Application (PACIIA), Wuhan, China."},{"key":"ref_50","unstructured":"Baudhuin, H., and Lambot, A. (2020). Change Detection in Satellite Imagery Using Deep Learning. [Master\u2019s Thesis, Universit\u00e9 Catholique de Louvain]."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3034752","article-title":"Remote sensing image change detection with transformers","volume":"60","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1007\/s10514-018-9734-5","article-title":"Street-view change detection with deconvolutional networks","volume":"42","author":"Alcantarilla","year":"2018","journal-title":"Auton. Robot."},{"key":"ref_53","unstructured":"Daudt, R.C., Le Saux, B., and Boulch, A. (2018, January 7\u201310). Fully convolutional siamese networks for change detection. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1846\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:46:27Z","timestamp":1760107587000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1846"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,22]]},"references-count":53,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16111846"],"URL":"https:\/\/doi.org\/10.3390\/rs16111846","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,22]]}}}