{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T19:39:47Z","timestamp":1774899587025,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,5,7]],"date-time":"2019-05-07T00:00:00Z","timestamp":1557187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFB0501501"],"award-info":[{"award-number":["2016YFB0501501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41331176 and 41371352"],"award-info":[{"award-number":["41331176 and 41371352"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the rapid development of urbanization in China, monitoring urban changes is of great significance to city management, urban planning, and cadastral map updating. Spaceborne synthetic aperture radar (SAR) sensors can capture a large area of radar images quickly with fine spatiotemporal resolution and are not affected by weather conditions, making multi-temporal SAR images suitable for change detection. In this paper, a new urban building change detection method based on an improved difference image and residual U-Net network is proposed. In order to overcome the intensity compression problem of the traditional log-ratio method, the spatial distance and intensity similarity are combined to generate a weighting function to obtain a weighted difference image. By fusing the weighted difference image and the bitemporal original images, the three-channel color difference image is generated for building change detection. Due to the complexity of urban environments and the small scale of building changes, the residual U-Net network is used instead of fixed statistical models and the construction and classifier of the network are modified to distinguish between different building changes. Three scenes of Sentinel-1 interferometric wide swath data are used to validate the proposed method. The experimental results and comparative analysis show that our proposed method is effective for urban building change detection and is superior to the original U-Net and SVM method.<\/jats:p>","DOI":"10.3390\/rs11091091","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T08:19:59Z","timestamp":1557389999000},"page":"1091","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Urban Building Change Detection in SAR Images Using Combined Differential Image and Residual U-Net Network"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1398-7196","authenticated-orcid":false,"given":"Lu","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4887-923X","authenticated-orcid":false,"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0088-8148","authenticated-orcid":false,"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9280-8378","authenticated-orcid":false,"given":"Fan","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,7]]},"reference":[{"key":"ref_1","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_2","unstructured":"United Nations (2018, October 25). World Urbanization Prospects\u2014The 2014 Revision. Available online: http:\/\/ esa.un.org\/unpd\/wup\/."},{"key":"ref_3","first-page":"116","article-title":"An unsupervised change detection approach based on K&I dual thresholds under the generalized gauss model assumption in SAR images","volume":"42","author":"Hu","year":"2013","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1080\/01431161.2013.871596","article-title":"Unsupervised change detection in sar images based on locally fitting model and semi-em algorithm","volume":"35","author":"Su","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3486","DOI":"10.1109\/JSTARS.2015.2416635","article-title":"Improved pixel-based change detection accuracy using an object-based approach in multitemporal SAR flood images","volume":"8","author":"Lu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"439","DOI":"10.5194\/isprs-annals-IV-2-W4-439-2017","article-title":"A SAR intensity images change detection method based on fusion difference detector and statistical properties","volume":"4","author":"Cui","year":"2017","journal-title":"ISPRS Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gao, F., Liu, X., Dong, J., Zhong, G., and Jian, M. (2017). Change detection in SAR images based on deep semi-NME and SVD networks. Remote Sens., 9.","DOI":"10.3390\/rs9050435"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1109\/TGRS.2007.893568","article-title":"A new statistical similarity measure for change detection in multitemporal sar images and its extension to multiscale change analysis","volume":"45","author":"Inglada","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1080\/014311698215649","article-title":"Speckle filtering in satellite SAR change detection imagery","volume":"19","author":"Dekker","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","first-page":"123","article-title":"An automatic change detection approach for rapid flood mapping in sentinel-1 sar data","volume":"73","author":"Li","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, C., Wang, S., Zhang, H., and Liu, M. (2017, January 23\u201328). SAR image change detection method based on visual attention. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127649"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1109\/TGRS.2002.805083","article-title":"Speckle removal from SAR images in the undecimated wavelet domain","volume":"40","author":"Argenti","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1080\/22797254.2018.1482523","article-title":"An improved neighborhood-based ratio approach for change detection in SAR images","volume":"51","author":"Zhuang","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhuang, H., Fan, H., Deng, K., and Yao, G. (2018). A spatial-temporal adaptive neighborhood-based ratio approach for change detection in SAR images. Remote Sens., 10.","DOI":"10.3390\/rs10081295"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4701","DOI":"10.1109\/JSTARS.2018.2866540","article-title":"SAR image change detection using saliency extraction and shearlet transform","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1109\/TGRS.2004.842441","article-title":"An unsupervised approach based on the generalized gaussian model to automatic change detection in multitemporal SAR images","volume":"43","author":"Bazi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ghanbari, M., and Akbari, V. (2015, January 26\u201331). Generalized minimum-error thresholding for unsupervised change detection from multilook polarimetric SAR data. In Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326153"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.02.013","article-title":"Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images","volume":"116","author":"Zhang","year":"2016","journal-title":"Isprs Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"559","DOI":"10.3390\/s18020559","article-title":"An unsupervised change detection method using time-series of PolSAR images from Radarsat-2 and GaoFen-3","volume":"18","author":"Liu","year":"2018","journal-title":"Sensors"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1080\/2150704X.2013.858841","article-title":"Unsupervised change detection using fuzzy -means and MRF from remotely sensed images","volume":"4","author":"Hao","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nielsen, A.A., and Vestergaard, J.S. (2015, January 22\u201324). Change detection in bi-temporal data by canonical information analysis. Proceedings of the 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp), Annecy, France.","DOI":"10.1109\/Multi-Temp.2015.7245779"},{"key":"ref_22","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."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/TFUZZ.2013.2249072","article-title":"Fuzzy clustering with a modified mrf energy function for change detection in synthetic aperture radar images","volume":"22","author":"Gong","year":"2014","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.isprsjprs.2017.05.001","article-title":"Feature learning and change feature classification based on deep learning for ternary change detection in SAR images","volume":"129","author":"Gong","year":"2017","journal-title":"Isprs Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/TNNLS.2015.2435783","article-title":"Change detection in synthetic aperture radar images based on deep neural networks","volume":"27","author":"Gong","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1109\/LGRS.2017.2786344","article-title":"Temporal change detection in SAR images using log cumulants and stacked autoencoder","volume":"15","author":"Gleich","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chini, M., Pelich, R., Hostache, R., Matgen, P., and Lopez-Martinez, C. (2018). Towards a 20 m global building map from Sentinel-1 SAR data. Remote Sens., 10.","DOI":"10.3390\/rs10111833"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wei, S., Zhang, H., Wang, C., Wang, Y., and Xu, L. (2019). Multi-temporal SAR data large-scale crop mapping based on U-Net model. Remote Sens., 11.","DOI":"10.3390\/rs11010068"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, Y., He, C., Liu, X., and Liao, M. (2018). A hierarchical fully convolutional network integrated with sparse and low-rank subspace representations for PolSAR imagery classification. Remote Sens., 10.","DOI":"10.3390\/rs10020342"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, C., and Zhang, H. (2018, January 18\u201321). Integrating h-a-\u03b1 with fully convolutional networks for fully PolSAR classification. Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958799"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Song, A., Choi, J., Han, Y., and Kim, Y. (2018). Change detection in hyperspectral images using recurrent 3d fully convolutional networks. Remote Sens., 10.","DOI":"10.3390\/rs10111827"},{"key":"ref_33","first-page":"483","article-title":"A novel approach to change detection in SAR images with CNN classification","volume":"6","author":"Xu","year":"2017","journal-title":"J. Radar."},{"key":"ref_34","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_35","first-page":"3436","article-title":"SAR image scene classification with fully convolutional network and modified conditional random field-recurrent neural network","volume":"36","author":"Tang","year":"2016","journal-title":"J. Comput. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tanase, R., Datcu, M., and Raducanu, D. (2016, January 10\u201315). A convolutional deep belief network for polarimetric SAR data feature extraction. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730968"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual U-Net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geos. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cao, H., Zhang, H., Wang, C., and Zhang, B. (2018). Operational built-up areas extraction for cities in china using Sentinel-1 SAR data. Remote Sens., 10.","DOI":"10.3390\/rs10060874"},{"key":"ref_39","first-page":"94","article-title":"SAR image change detection methods based on glcm texture features","volume":"32","author":"Han","year":"2012","journal-title":"J. Geod. Geodyn."},{"key":"ref_40","unstructured":"(2019, May 02). Nanjing Bureau of Planning and natural Resources, Available online: http:\/\/ghj.nanjing.gov.cn\/ztzl\/ghbz\/ztgh\/."},{"key":"ref_41","first-page":"6859","article-title":"The Jeffries\u2013Matusita distance for the case of complex Wishart distribution as a separability criterion for fully polarimetric SAR data","volume":"35","author":"Dabboor","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, Y., Qi, Q., and Liu, Y. (2018). Unsupervised segmentation evaluation using area-weighted variance and jeffries-matusita distance for remote sensing images. Remote Sens., 10.","DOI":"10.3390\/rs10081193"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"De Grandi, E., Mitchard, E., and Hoekman, D. (2016). Wavelet based analysis of TanDEM-X and LiDAR DEMs across a tropical vegetation heterogeneity gradient driven by fire disturbance in indonesia. Remote Sens., 8.","DOI":"10.3390\/rs8080641"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/9\/1091\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:49:54Z","timestamp":1760186994000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/9\/1091"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,7]]},"references-count":44,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["rs11091091"],"URL":"https:\/\/doi.org\/10.3390\/rs11091091","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,7]]}}}