{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T11:23:33Z","timestamp":1774524213295,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T00:00:00Z","timestamp":1662768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018YFB0505300"],"award-info":[{"award-number":["2018YFB0505300"]}]},{"name":"National Key Research and Development Program of China","award":["41701472"],"award-info":[{"award-number":["41701472"]}]},{"name":"National Key Research and Development Program of China","award":["42071316"],"award-info":[{"award-number":["42071316"]}]},{"name":"National Key Research and Development Program of China","award":["41971375"],"award-info":[{"award-number":["41971375"]}]},{"name":"National Science Foundation of China","award":["2018YFB0505300"],"award-info":[{"award-number":["2018YFB0505300"]}]},{"name":"National Science Foundation of China","award":["41701472"],"award-info":[{"award-number":["41701472"]}]},{"name":"National Science Foundation of China","award":["42071316"],"award-info":[{"award-number":["42071316"]}]},{"name":"National Science Foundation of China","award":["41971375"],"award-info":[{"award-number":["41971375"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Change detection extracts change areas in bitemporal remote sensing images, and plays an important role in urban construction and coordination. However, due to image offsets and brightness differences in bitemporal remote sensing images, traditional change detection algorithms often have reduced applicability and accuracy. The development of deep learning-based algorithms has improved their applicability and accuracy; however, existing models use either convolutions or transformers in the feature encoding stage. During feature extraction, local fine features and global features in images cannot always be obtained simultaneously. To address these issues, we propose a novel end-to-end change detection network (EGCTNet) with a fusion encoder (FE) that combines convolutional neural network (CNN) and transformer features. An intermediate decoder (IMD) eliminates global noise introduced during the encoding stage. We noted that ground objects have clearer semantic information and improved edge features. Therefore, we propose an edge detection branch (EDB) that uses object edges to guide mask features. We conducted extensive experiments on the LEVIR-CD and WHU-CD datasets, and EGCTNet exhibits good performance in detecting small and large building objects. On the LEVIR-CD dataset, we obtain F1 and IoU scores of 0.9008 and 0.8295. On the WHU-CD dataset, we obtain F1 and IoU scores of 0.9070 and 0.8298. Experimental results show that our model outperforms several previous change detection methods.<\/jats:p>","DOI":"10.3390\/rs14184524","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T04:05:41Z","timestamp":1663041941000},"page":"4524","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Building Change Detection Based on an Edge-Guided Convolutional Neural Network Combined with a Transformer"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3190-0178","authenticated-orcid":false,"given":"Liegang","family":"Xia","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}]},{"given":"Jun","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}]},{"given":"Jiancheng","family":"Luo","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Junxia","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}]},{"given":"Dezhi","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}]},{"given":"Zhanfeng","family":"Shen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,10]]},"reference":[{"key":"ref_1","unstructured":"J\u00e9r\u00f4me, T. (2022). Change Detection. Springer Handbook of Geographic Information, Springer."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"1913","DOI":"10.1109\/JSTARS.2012.2228469","article-title":"Seasonal Change of Land-Use\/Land-Cover (Lulc) Detection Using Modis Data in Rapid Urbanization Regions: A Case Study of the Pearl River Delta Region (China)","volume":"6","author":"Hu","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jensen, J.R., and Im, J. (2007). Remote Sensing Change Detection in Urban Environments. Geo-Spatial Technologies in Urban Environments, Springer.","DOI":"10.1007\/978-3-540-69417-5"},{"key":"ref_5","unstructured":"Zhang, J.-F., Xie, L.-L., and Tao, X.-X. (2003, January 21\u201325). Change Detection of Earthquake-Damaged Buildings on Remote Sensing Image and Its Application in Seismic Disaster Assessment. Proceedings of the IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No. 03CH37477), Toulouse, France."},{"key":"ref_6","unstructured":"Bitelli, G., Camassi, R., Gusella, L., and Mognol, A. (2004, January 12\u201323). Image Change Detection on Urban Area: The Earthquake Case. Proceedings of the XXth ISPRS Congress, Istanbul, Turkey."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1109\/36.718860","article-title":"The Effects of Image Misregistration on the Accuracy of Remotely Sensed Change Detection","volume":"36","author":"Dai","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1109\/TIP.2004.838698","article-title":"Image Change Detection Algorithms: A Systematic Survey","volume":"14","author":"Radke","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"225","DOI":"10.14358\/PERS.83.3.225","article-title":"Unsupervised Object-Based Differencing for Land-Cover Change Detection","volume":"83","author":"Zhu","year":"2017","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1109\/LGRS.2012.2191387","article-title":"Wavelet Fusion on Ratio Images for Change Detection in Sar Images","volume":"9","author":"Ma","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/0034-4257(95)00233-2","article-title":"An Assessment of Several Linear Change Detection Techniques for Mapping Forest Mortality Using Multitemporal Landsat Tm Data","volume":"56","author":"Collins","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1109\/TGRS.2006.885408","article-title":"A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in the Polar Domain","volume":"45","author":"Bovolo","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, Q., and Chen, Y. (2016). Multi-Feature Object-Based Change Detection Using Self-Adaptive Weight Change Vector Analysis. Remote Sens., 8.","DOI":"10.3390\/rs8070549"},{"key":"ref_14","first-page":"1649","article-title":"Application of Principal Components Analysis to Change Detection","volume":"53","author":"Fung","year":"1987","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","article-title":"Change Detection from Remotely Sensed Images: From Pixel-Based to Object-Based Approaches","volume":"80","author":"Hussain","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4434","DOI":"10.1080\/01431161.2011.648285","article-title":"Object-Based Change Detection","volume":"33","author":"Chen","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lang, S. (2008). Object-Based Image Analysis for Remote Sensing Applications: Modeling Reality\u2014Dealing with Complexity. Object-Based Image Analysis, Springer.","DOI":"10.1007\/978-3-540-77058-9_1"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.isprsjprs.2013.10.007","article-title":"Assessment of the Image Misregistration Effects on Object-Based Change Detection","volume":"87","author":"Chen","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1109\/LGRS.2019.2892432","article-title":"A Post-Classification Comparison Method for Sar and Optical Images Change Detection","volume":"16","author":"Wan","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2016.01.018","article-title":"A Targeted Change-Detection Procedure by Combining Change Vector Analysis and Post-Classification Approach","volume":"114","author":"Ye","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, C., Wei, S., Ji, S., and Lu, M. (2019). Detecting Large-Scale Urban Land Cover Changes from Very High Resolution Remote Sensing Images Using Cnn-Based Classification. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8040189"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, H., and Shi, Z. (2020). A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection. Remote Sens., 12.","DOI":"10.3390\/rs12101662"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jiang, H., Hu, X., Li, K., Zhang, J., Gong, J., and Zhang, M. (2020). Pga-Siamnet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection. Remote Sens., 12.","DOI":"10.3390\/rs12030484"},{"key":"ref_24","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":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","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":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2021.03.005","article-title":"Clnet: Cross-Layer Convolutional Neural Network for Change Detection in Optical Remote Sensing Imagery","volume":"175","author":"Zheng","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","unstructured":"Daudt, R.C., Saux, B.L., 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."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"30925","DOI":"10.1109\/ACCESS.2022.3160163","article-title":"Msf-Net: A Multiscale Supervised Fusion Network for Building Change Detection in High-Resolution Remote Sensing Images","volume":"10","author":"Chen","year":"2022","journal-title":"IEEE Access"},{"key":"ref_29","first-page":"1","article-title":"Edge-Guided Recurrent Convolutional Neural Network for Multitemporal Remote Sensing Image Building Change Detection","volume":"60","author":"Bai","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bandara, W.G.C., and Patel, V.M. (2022). A Transformer-Based Siamese Network for Change Detection. arXiv.","DOI":"10.1109\/IGARSS46834.2022.9883686"},{"key":"ref_31","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_32","unstructured":"Wei, Y., Zhao, Z., and Song, J. (2004, January 20\u201324). Urban Building Extraction from High-Resolution Satellite Panchromatic Image Using Clustering and Edge Detection. Proceedings of the IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA."},{"key":"ref_33","first-page":"1","article-title":"Boundary Enhancement Semantic Segmentation for Building Extraction from Remote Sensed Image","volume":"60","author":"Jung","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 26\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sunkara, R., and Luo, T. (2022). No More Strided Convolutions or Pooling: A New Cnn Building Block for Low-Resolution Images and Small Objects. arXiv.","DOI":"10.1007\/978-3-031-26409-2_27"},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","article-title":"Fully Convolutional Networks for Multisource Building Extraction from an Open Aerial and Satellite Imagery Data Set","volume":"57","author":"Ji","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4898","DOI":"10.1080\/01431161.2021.1906982","article-title":"Nestnet: A Multiscale Convolutional Neural Network for Remote Sensing Image Change Detection","volume":"42","author":"Yu","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4524\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:28:53Z","timestamp":1760142533000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4524"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,10]]},"references-count":39,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14184524"],"URL":"https:\/\/doi.org\/10.3390\/rs14184524","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,10]]}}}