{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T19:57:09Z","timestamp":1767988629224,"version":"3.49.0"},"reference-count":58,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T00:00:00Z","timestamp":1659744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Administration of Science, Technology and Industry for National","award":["06-Y30F04-9001-2022"],"award-info":[{"award-number":["06-Y30F04-9001-2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Building change detection is a prominent topic in remote sensing applications. Scholars have proposed a variety of fully-convolutional-network-based change detection methods for high-resolution remote sensing images, achieving impressive results on several building datasets. However, existing methods cannot solve the problem of pseudo-changes caused by factors such as \u201csame object with different spectrums\u201d and \u201cdifferent objects with same spectrums\u201d in high-resolution remote sensing images because their networks are constructed using simple similarity measures. To increase the ability of the model to resist pseudo-changes and improve detection accuracy, we propose an improved method based on fully convolutional network, called multitask difference-enhanced Siamese network (MDESNet) for building change detection in high-resolution remote sensing images. We improved its feature extraction ability by adding semantic constraints and effectively utilized features while improving its recognition performance. Furthermore, we proposed a similarity measure combining concatenation and difference, called the feature difference enhancement (FDE) module, and designed comparative experiments to demonstrate its effectiveness in resisting pseudo-changes. Using the building change detection dataset (BCDD), we demonstrate that our method outperforms other state-of-the-art change detection methods, achieving the highest F1-score (0.9124) and OA (0.9874), indicating its advantages for high-resolution remote sensing image building change detection tasks.<\/jats:p>","DOI":"10.3390\/rs14153775","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T04:16:55Z","timestamp":1660018615000},"page":"3775","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["MDESNet: Multitask Difference-Enhanced Siamese Network for Building Change Detection in High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Jiaxiang","family":"Zheng","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yichen","family":"Tian","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Chao","family":"Yuan","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Kai","family":"Yin","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Feifei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Fangmiao","family":"Chen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Qiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep Learning-Based Classification of Hyperspectral Data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.drugpo.2011.02.001","article-title":"Opium poppy monitoring with remote sensing in North Myanmar","volume":"22","author":"Tian","year":"2011","journal-title":"Int. J. Drug Policy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10593","DOI":"10.3390\/rs61110593","article-title":"Examining Land Use and Land Cover Spatiotemporal Change and Driving Forces in Beijing from 1978 to 2010","volume":"6","author":"Tian","year":"2014","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"195","DOI":"10.3390\/su7010195","article-title":"Evaluation of Carbon and Oxygen Balances in Urban Ecosystems Using Land Use\/Land Cover and Statistical Data","volume":"7","author":"Yin","year":"2014","journal-title":"Sustainability"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xue, J., Xu, H., Yang, H., Wang, B., Wu, P., Choi, J., Cai, L., and Wu, Y. (2021). Multi-Feature Enhanced Building Change Detection Based on Semantic Information Guidance. Remote Sens., 13.","DOI":"10.3390\/rs13204171"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, H., Lv, X., Zhang, K., and Guo, B. (2022). Building Change Detection Based on 3D Co-Segmentation Using Satellite Stereo Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14030628"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/JPROC.2012.2197169","article-title":"A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images","volume":"101","author":"Bruzzone","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1109\/LGRS.2004.837009","article-title":"Exploiting spectral and spatial information in hyperspectral urban data with high resolution","volume":"1","author":"Gamba","year":"2004","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","first-page":"1","article-title":"Interpretation Theory and Application Method Development for Information Extraction from High Resolution Remotely Sensed Data","volume":"10","author":"Gong","year":"2006","journal-title":"J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"803","DOI":"10.14358\/PERS.70.7.803","article-title":"Wavelets for urban spatial feature discrimination: Comparisons with fractal, spatial autocorrelation, and spatial co-occurrence approaches","volume":"70","author":"Myint","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_12","first-page":"1533","article-title":"Change detection in coastal zone environment","volume":"12","author":"Weismiller","year":"1978","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s40789-018-0195-4","article-title":"Vegetation change detection research of Dunhuang city based on GF-1 data","volume":"5","author":"Zhang","year":"2018","journal-title":"Int. J. Coal Sci. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Review Article Digital change detection techniques using remotely-sensed data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1109\/36.843009","article-title":"Automatic analysis of the difference image for unsupervised change detection","volume":"38","author":"Bruzzone","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","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 Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"255","DOI":"10.14358\/PERS.76.3.255","article-title":"Land-cover change detection using one-class support vector machine","volume":"76","author":"Li","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Seo, D., Kim, Y., Eo, Y., Park, W., and Park, H. (2017). Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection. Remote Sens., 9.","DOI":"10.3390\/rs9111163"},{"key":"ref_19","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_20","first-page":"90","article-title":"Visualizing 3-D texture: A three-dimensional structural approach to model forest texture","volume":"20","author":"Hay","year":"1994","journal-title":"Can. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lefebvre, A., Corpetti, T., and Hubert-Moy, L. (2008, January 6\u201311). Object-Oriented Approach and Texture Analysis for Change Detection in Very High Resolution Images. Proceedings of the 2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4779809"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.rse.2016.02.030","article-title":"A new approach for land cover classification and change analysis: Integrating backdating and an object-based method","volume":"177","author":"Yu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.3390\/s8031613","article-title":"Object-based Land Cover Classification and Change Analysis in the Baltimore Metropolitan Area Using Multitemporal High Resolution Remote Sensing Data","volume":"8","author":"Zhou","year":"2008","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jung, S., Lee, W.H., and Han, Y. (2021). Change Detection of Building Objects in High-Resolution Single-Sensor and Multi-Sensor Imagery Considering the Sun and Sensor\u2019s Elevation and Azimuth Angles. Remote Sens., 13.","DOI":"10.3390\/rs13183660"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_27","unstructured":"Daudt, R.C., Saux, B.L., and Boulch, A. (2018). Fully Convolutional Siamese Networks for Change Detection. arXiv."},{"key":"ref_28","unstructured":"Papadomanolaki, M., Verma, S., Vakalopoulou, M., Gupta, S., and Karantzalos, K. (August, January 28). Detecting urban changes with recurrent neural networks from multitemporal Sentinel-2 data. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, S., and Huo, L. (2021, January 11\u201316). Remote Sensing Image Change Detection Based on Fully Convolutional Network With Pyramid Attention. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554522"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Varghese, A., Gubbi, J., Ramaswamy, A., and Balamuralidhar, P. (2019). ChangeNet: A Deep Learning Architecture for Visual Change Detection. Proceedings of the Computer Vision\u2014ECCV 2018 Workshops, Springer.","DOI":"10.1007\/978-3-030-11012-3_10"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4139","DOI":"10.1109\/JSTARS.2021.3069242","article-title":"Local Similarity Siamese Network for Urban Land Change Detection on Remote Sensing Images","volume":"14","author":"Lee","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","first-page":"1","article-title":"A Combined Loss-Based Multiscale Fully Convolutional Network for High-Resolution Remote Sensing Image Change Detection","volume":"19","author":"Li","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","unstructured":"Liu, Y., Pang, C., Zhan, Z., Zhang, X., and Yang, X. (2019). Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lin, T., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Roy, A.G., Navab, N., and Wachinger, C. (2018). Concurrent Spatial and Channel \u2019Squeeze & Excitation\u2019 in Fully Convolutional Networks. arXiv.","DOI":"10.1007\/978-3-030-00928-1_48"},{"key":"ref_36","unstructured":"Bromley, J., Guyon, I., LeCun, Y., S\u00e4ckinger, E., and Shah, R. (December, January 29). Signature verification using a \u201cSiamese\u201d time delay neural network. Proceedings of the Proceedings of the 6th International Conference on Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_37","unstructured":"Chopra, S., Hadsell, R., and LeCun, Y. (2005, January 20\u201325). Learning a similarity metric discriminatively, with application to face verification. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Benajiba, Y., Sun, J., Zhang, Y., Jiang, L., Weng, Z., and Biran, O. (2018). Siamese Networks for Semantic Pattern Similarity. arXiv.","DOI":"10.1109\/ICOSC.2019.8665512"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ranasinghe, T., Orasan, C., and Mitkov, R. (2019, January 2\u20134). Semantic Textual Similarity with Siamese Neural Networks. Proceedings of the International Conference on Recent Advances in Natural Language Processing, Varna, Bulgaria.","DOI":"10.26615\/978-954-452-056-4_116"},{"key":"ref_40","unstructured":"Zhang, H., Wu, C., Zhang, Z., Zhu, Y., Zhang, Z.-L., Lin, H., Sun, Y., He, T., Mueller, J., and Manmatha, R. (2020). ResNeSt: Split-Attention Networks. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2016). Aggregated Residual Transformations for Deep Neural Networks. arXiv.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2017). Squeeze-and-Excitation Networks. arXiv.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Girshick, R., and He, K. (2019, January 15\u201320). Panoptic Feature Pyramid Networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00656"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zhong, Y., and Wang, J. (2020, January 13\u201319). Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00415"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, X., He, L., Qin, K., Dang, Q., Si, H., Tang, X., and Jiao, L. (2022). SMD-Net: Siamese Multi-Scale Difference-Enhancement Network for Change Detection in Remote Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14071580"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zheng, D., Wei, Z., Wu, Z., and Liu, J. (2022). Learning Pairwise Potential CRFs in Deep Siamese Network for Change Detection. Remote Sens., 14.","DOI":"10.3390\/rs14040841"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","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":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A., and Agrawal, A. (2018, January 18\u201323). Context Encoding for Semantic Segmentation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00747"},{"key":"ref_51","unstructured":"Kingma, D.P., and Ba, J.J.A. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_52","unstructured":"Loshchilov, I., and ArXiv, F.H.J. (2017). SGDR: Stochastic Gradient Descent with Warm Restarts. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"7232","DOI":"10.1109\/TGRS.2020.2981051","article-title":"A Feature Difference Convolutional Neural Network-Based Change Detection Method","volume":"58","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","first-page":"1","article-title":"SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images","volume":"19","author":"Fang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_55","unstructured":"Olaf, R., Philipp, F., and Thomas, B. (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."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Md Mahfuzur Rahman, S., Nima, T., and Jianming, L. (2018). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Proceedings of the DLMIA: International Workshop on Deep Learning in Medical Image Analysis, Granada, Spain, 20 September 2018, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201316). Pyramid Scene Parsing Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Liang-Chieh, C., Yukun, Z., George, P., Florian, S., and Hartwig, A. (2018, January 18\u201323). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1007\/978-3-030-01234-2_49"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3775\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:05:04Z","timestamp":1760141104000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3775"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,6]]},"references-count":58,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14153775"],"URL":"https:\/\/doi.org\/10.3390\/rs14153775","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,6]]}}}