{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T07:56:34Z","timestamp":1781164594938,"version":"3.54.1"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,28]],"date-time":"2022-01-28T00:00:00Z","timestamp":1643328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"LuTan-1 L-Band Spaceborne Bistatic SAR Data Processing Program","award":["E0H2080702"],"award-info":[{"award-number":["E0H2080702"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Building change detection using remote sensing images is significant to urban planning and city monitoring. The height information extracted from very high resolution (VHR) satellite stereo images provides valuable information for the detection of 3D changes in urban buildings. However, most existing 3D change detection algorithms are based on the independent segmentation of two-temporal images and the feature fusion of spectral change and height change. These methods do not consider 3D change information and spatial context information simultaneously. In this paper, we propose a novel building change detection algorithm based on 3D Co-segmentation, which makes full use of the 3D change information contained in the stereoscope data. An energy function containing spectral change information, height change information, and spatial context information is constructed. Image change feature is extracted using morphological building index (MBI), and height change feature is obtained by robust normalized digital surface models (nDSM) difference. 3D Co-segmentation divides the two-temporal images into the changed foreground and unchanged background through the graph-cut-based energy minimization method. The object-to-object detection results are obtained through overlay analysis, and the quantitative height change values are calculated according to this correspondence. The superiority of the proposed algorithm is that it can obtain the changes of buildings in planar and vertical simultaneously. The performance of the algorithm is evaluated in detail using six groups of satellite datasets. The experimental results prove the effectiveness of the proposed building change detection algorithm.<\/jats:p>","DOI":"10.3390\/rs14030628","type":"journal-article","created":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T01:43:27Z","timestamp":1643420607000},"page":"628","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Building Change Detection Based on 3D Co-Segmentation Using Satellite Stereo Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3940-3862","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6991-583X","authenticated-orcid":false,"given":"Xiaolei","family":"Lv","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2413-6370","authenticated-orcid":false,"given":"Kaiyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Guo","sequence":"additional","affiliation":[{"name":"Beijing Capital International Airport Group, Beijing Daxing International Airport, Beijing 102604, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/TPAMI.2007.1166","article-title":"Stereo processing by semiglobal matching and mutual information","volume":"30","author":"Hirschmuller","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","first-page":"1137","article-title":"Towards automated DEM generation from high resolution stereo satellite images","volume":"37","author":"Lehner","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. Int. Soc. Photogramm. Remote Sens."},{"key":"ref_3","unstructured":"d\u2019Angelo, P., and Reinartz, P. (2011). Semiglobal matching results on the ISPRS stereo matching benchmark. High-Resolution Earth Imaging for Geospatial Information, Institute of Photogrammetry and GeoInformation, Leibniz Universit\u00e4t Hannover."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.isprsjprs.2016.09.013","article-title":"3D change detection\u2013approaches and applications","volume":"122","author":"Qin","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1109\/TGRS.2016.2627638","article-title":"Cosegmentation for object-based building change detection from high-resolution remotely sensed images","volume":"55","author":"Xiao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rse.2017.09.022","article-title":"Separate segmentation of multi-temporal high-resolution remote sensing images for object-based change detection in urban area","volume":"201","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, J., Liu, H., Hou, J., Yang, M., and Deng, M. (2018). Improving Building Change Detection in VHR Remote Sensing Imagery by Combining Coarse Location and Co-Segmentation. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7060213"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gong, J., Hu, X., Pang, S., and Li, K. (2019). Patch Matching and Dense CRF-Based Co-Refinement for Building Change Detection from Bi-Temporal Aerial Images. Sensors, 19.","DOI":"10.3390\/s19071557"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"543","DOI":"10.14358\/PERS.85.8.543","article-title":"Roof-Cut Guided Localization for Building Change Detection from Imagery and Footprint Map","volume":"85","author":"Gong","year":"2019","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhu, L., Zhang, J., and Sun, Y. (2021). Remote Sensing Image Change Detection Using Superpixel Cosegmentation. Information, 12.","DOI":"10.3390\/info12020094"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4005","DOI":"10.1080\/01431161.2021.1881182","article-title":"An improved graph-cut-based unsupervised change detection method for multispectral remote-sensing images","volume":"42","author":"Hao","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, K., Fu, X., Lv, X., and Yuan, J. (2021). Unsupervised Multitemporal Building Change Detection Framework Based on Cosegmentation Using Time-Series SAR. Remote Sens., 13.","DOI":"10.3390\/rs13030471"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gstaiger, V., Tian, J., Kiefl, R., and Kurz, F. (2018). 2d vs. 3d change detection using aerial imagery to support crisis management of large-scale events. Remote Sens., 10.","DOI":"10.3390\/rs10122054"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tian, J., Chaabouni-Chouayakh, H., and Reinartz, P. (2011, January 10\u201312). 3D building change detection from high resolution spaceborne stereo imagery. Proceedings of the 2011 International Workshop on Multi-Platform\/Multi-Sensor Remote Sensing and Mapping, Xiamen, China.","DOI":"10.1109\/M2RSM.2011.5697371"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tian, J., and Reinartz, P. (2011, January 9\u201311). Multitemporal 3D change detection in urban areas using stereo information from different sensors. Proceedings of the 2011 International Symposium on Image and Data Fusion, Tengchong, China.","DOI":"10.1109\/ISIDF.2011.6024215"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1109\/TGRS.2013.2240692","article-title":"Building change detection based on satellite stereo imagery and digital surface models","volume":"52","author":"Tian","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","unstructured":"Tian, J., and Reinartz, P. (2014, January 7\u201310). Dempster-Shafer fusion based building change detection from satellite stereo imagery. Proceedings of the 17th International Conference on Information Fusion (FUSION), Salamanca, Spain."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tian, J., Reinartz, P., and Dezert, J. (April, January 30). Building change detection in satellite stereo imagery based on belief functions. Proceedings of the 2015 Joint Urban Remote Sensing Event (JURSE), Lausanne, Switzerland.","DOI":"10.1109\/JURSE.2015.7120482"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tian, J., Dezert, J., and Reinartz, P. (2015, January 14\u201316). Refined building change detection in satellite stereo imagery based on belief functions and reliabilities. Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), San Diego, CA, USA.","DOI":"10.1109\/MFI.2015.7295802"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tian, J., Dezert, J., and Qin, R. (2018, January 10\u201313). Time-Series 3D Building Change Detection Based on Belief Functions. Proceedings of the 2018 21st International Conference on Information Fusion (FUSION), Cambridge, UK.","DOI":"10.23919\/ICIF.2018.8455206"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/19479832.2018.1513957","article-title":"Fusion of multispectral imagery and DSMs for building change detection using belief functions and reliabilities","volume":"10","author":"Tian","year":"2019","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.isprsjprs.2013.02.017","article-title":"Region-based automatic building and forest change detection on Cartosat-1 stereo imagery","volume":"79","author":"Tian","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7911","DOI":"10.3390\/rs6097911","article-title":"An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images","volume":"6","author":"Qin","year":"2014","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.isprsjprs.2014.07.007","article-title":"Change detection on LOD 2 building models with very high resolution spaceborne stereo imagery","volume":"96","author":"Qin","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tian, J., Gharibbafghi, Z., and Reinartz, P. (2019, January 16\u201317). Superpixel-Based 3D Building Model Refinement and Change Detection, Using VHR Stereo Satellite Imagery. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00069"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wen, D., Huang, X., Zhang, A., and Ke, X. (2019). Monitoring 3D Building Change and Urban Redevelopment Patterns in Inner City Areas of Chinese Megacities Using Multi-View Satellite Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11070763"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111802","DOI":"10.1016\/j.rse.2020.111802","article-title":"An Automatic Change Detection Method for Monitoring Newly Constructed Building Areas Using Time-Series Multi-View High-Resolution Optical Satellite Images","volume":"244","author":"Huang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"259","DOI":"10.5194\/isprsarchives-XL-3-259-2014","article-title":"A supervised method for object-based 3d building change detection on aerial stereo images","volume":"40","author":"Qin","year":"2014","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.neucom.2015.11.118","article-title":"Building change detection with RGB-D map generated from UAV images","volume":"208","author":"Chen","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"149","DOI":"10.5194\/isprs-annals-III-7-149-2016","article-title":"Building Change Detection in Very High Resolution Satellite Stereo Image Time Series","volume":"3","author":"Tian","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2016"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3455","DOI":"10.1080\/01431161.2015.1066527","article-title":"Spatiotemporal inferences for use in building detection using series of very-high-resolution space-borne stereo images","volume":"37","author":"Qin","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yuan, X., Tian, J., and Reinartz, P. (2019, January 22\u201324). Building Change Detection Based on Deep Learning and Belief Function. Proceedings of the Joint Urban Remote Sensing Event 2019, Vannes, France.","DOI":"10.1109\/JURSE.2019.8808968"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pang, S., Hu, X., Cai, Z., Gong, J., and Zhang, M. (2018). Building change detection from bi-temporal dense-matching point clouds and aerial images. Sensors, 18.","DOI":"10.3390\/s18040966"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pang, S., Hu, X., Zhang, M., Cai, Z., and Liu, F. (2019). Co-Segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-Temporal Digital Surface Models and Aerial Images. Remote Sens., 11.","DOI":"10.3390\/rs11060729"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8310","DOI":"10.3390\/rs6098310","article-title":"Building Change Detection from Historical Aerial Photographs Using Dense Image Matching and Object-Based Image Analysis","volume":"6","author":"Nebiker","year":"2014","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"651","DOI":"10.5194\/isprsarchives-XL-7-W3-651-2015","article-title":"3D-information fusion from very high resolution satellite sensors","volume":"XL-7\/W3","author":"Krauss","year":"2015","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.1016\/j.asr.2020.05.041","article-title":"An Object Based Framework for Building Change Analysis Using 2D and 3D Information of High Resolution Satellite Images","volume":"66","author":"Mohammadi","year":"2020","journal-title":"Adv. Space Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s12518-020-00349-w","article-title":"Three-Dimensional Building Change Detection Using Object-Based Image Analysis (Case Study: Tehran)","volume":"13","author":"Hosseini","year":"2021","journal-title":"Appl. Geomat."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/34.969114","article-title":"Fast approximate energy minimization via graph cuts","volume":"23","author":"Boykov","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"721","DOI":"10.14358\/PERS.77.7.721","article-title":"A Multidirectional and Multiscale Morphological Index for Automatic Building Extraction from Multispectral GeoEye-1 Imagery","volume":"77","author":"Huang","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/JSTARS.2011.2168195","article-title":"Morphological Building\/Shadow Index for Building Extraction From High-Resolution Imagery Over Urban Areas","volume":"5","author":"Huang","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"53","DOI":"10.14358\/PERS.69.1.53","article-title":"Bias Compensation in Rational Functions for Ikonos Satellite Imagery","volume":"69","author":"Fraser","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"909","DOI":"10.14358\/PERS.71.8.909","article-title":"Bias-Compensated RPCs for Sensor Orientation of High-Resolution Satellite Imagery","volume":"71","author":"Fraser","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"59","DOI":"10.14358\/PERS.69.1.59","article-title":"Block Adjustment of High-Resolution Satellite Images Described by Rational Polynomials","volume":"69","author":"Grodecki","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_45","unstructured":"Arefi, H., and Hahn, M. (2005, January 12\u201314). A morphological reconstruction algorithm for separating off-terrain points from terrain points in laser scanning data. Proceedings of the ISPRS WG III\/3, III\/4, V\/3 Workshop \u201cLaser scanning 2005\u201d, Enschede, The Netherlands."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s11263-006-7934-5","article-title":"Graph Cuts and Efficient N-D Image Segmentation","volume":"70","author":"Boykov","year":"2006","journal-title":"Int. J. Comput. Vis."},{"key":"ref_47","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."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1109\/TPAMI.2004.60","article-title":"An experimental comparison of min-cut\/max-flow algorithms for energy minimization in vision","volume":"26","author":"Boykov","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/628\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:09:36Z","timestamp":1760134176000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/628"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,28]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030628"],"URL":"https:\/\/doi.org\/10.3390\/rs14030628","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,28]]}}}