{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T21:36:52Z","timestamp":1774129012663,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T00:00:00Z","timestamp":1614038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2016YFB0501404"],"award-info":[{"award-number":["2016YFB0501404"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The application of cosegmentation in remote sensing image change detection can effectively overcome the salt and pepper phenomenon and generate multitemporal changing objects with consistent boundaries. Cosegmentation considers the image information, such as spectrum and texture, and mines the spatial neighborhood information between pixels. However, each pixel in the minimum cut\/maximum flow algorithm for cosegmentation change detection is regarded as a node in the network flow diagram. This condition leads to a direct correlation between computation times and the number of nodes and edges in the diagram. It requires a large amount of computation and consumes excessive time for change detection of large areas. A superpixel segmentation method is combined into cosegmentation to solve this shortcoming. Simple linear iterative clustering is adopted to group pixels by using the similarity of features among pixels. Two-phase superpixels are overlaid to form the multitemporal consistent superpixel segmentation. Each superpixel block is regarded as a node for cosegmentation change detection, so as to reduce the number of nodes in the network flow diagram constructed by minimum cut\/maximum flow. In this study, the Chinese GF-1 and Landsat satellite images are taken as examples, the overall accuracy of the change detection results is above 0.80, and the calculation time is only one-fifth of the original.<\/jats:p>","DOI":"10.3390\/info12020094","type":"journal-article","created":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T12:40:16Z","timestamp":1614084016000},"page":"94","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Remote Sensing Image Change Detection Using Superpixel Cosegmentation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7783-4175","authenticated-orcid":false,"given":"Ling","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Sun","sequence":"additional","affiliation":[{"name":"Beijing Institute of Surveying and Mapping, Beijing 100038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3465","DOI":"10.1073\/pnas.1100480108","article-title":"Global land use change, economic globalization, and the looming land scarcity","volume":"108","author":"Lambin","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"You, Y., Cao, J., and Zhou, W. (2020). A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios. Remote Sens., 12.","DOI":"10.3390\/rs12152460"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Ma, L., Fu, T., Zhang, G., and Li, M. (2018). Change Detection in Coral Reef Environment Using High-Resolution Images: Comparison of Object-Based and Pixel-Based Paradigms. Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7110441"},{"key":"ref_4","unstructured":"Jin, X.Y. (2007). A Segmentation-Based Image Processing System. (20090123070A1), U.S. Patent."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40998-019-00251-1","article-title":"Image Segmentation Using Multilevel Thresholding: A Research Review","volume":"44","author":"Pare","year":"2019","journal-title":"Iran. J. Sci. Technol. Trans. Electr. Eng."},{"key":"ref_6","first-page":"12","article-title":"Multiresolution segmentation: An optimization approach for high quality multi-scale image segmentation","volume":"58","author":"Baatz","year":"2000","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2987","DOI":"10.1109\/TGRS.2014.2367129","article-title":"Marker-controlled watershed-based segmentation of multiresolution remote sensing images","volume":"53","author":"Gaetano","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.eswa.2017.04.018","article-title":"River channel segmentation in polarimetric SAR images: Watershed transform combined with average contrast maximisation","volume":"82","author":"Ciecholewski","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, G.B., Sun, Z.W., and Zhang, L. (2020). Road Identification Algorithm for Remote Sensing Images Based on Wavelet Transform and Recursive Operator. IEEE Access, 8.","DOI":"10.1109\/ACCESS.2020.3012997"},{"key":"ref_10","first-page":"218","article-title":"SegOptim-A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data","volume":"76","author":"Gonalves","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6418","DOI":"10.1080\/01431161.2019.1594431","article-title":"Extracting multi-features and optimizing feature space with sparse auto-encoder over WorldView-2 images","volume":"40","author":"Yang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1109\/LGRS.2017.2702062","article-title":"A Median regularized level set for hierarchical segmentation of SAR images","volume":"14","author":"Braga","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4565","DOI":"10.1109\/JSTARS.2017.2716620","article-title":"Level Set Segmentation Algorithm for High-Resolution Polarimetric SAR Images Based on a Heterogeneous Clutter Model","volume":"10","author":"Jin","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yuan, X.Y., Shi, J.F., and Gu, L.C. (2020). A Review of Deep Learning Methods for Semantic Segmentation of Remote Sensing Imagery. Expert Syst. Appl., 169.","DOI":"10.1016\/j.eswa.2020.114417"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hoeser, T., Bachofer, F., and Kuenzer, C. (2020). Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part II: Applications. Remote Sens., 12.","DOI":"10.3390\/rs12183053"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Niemeyer, I., Marpu, P.R., and Nussbaum, S. (2007, January 23\u201327). Change detection using the object features. Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4423319"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/34.1000236","article-title":"Mean Shift: A Robust Approach Toward Feature Space Analysis","volume":"24","author":"Comaniciu","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huang, J., Liu, Y., Wang, M., Zheng, Y., Wang, J., and Ming, D. (2019). Change Detection of High Spatial Resolution Images Based on Region-Line Primitive Association Analysis and Evidence Fusion. Remote Sens., 11.","DOI":"10.3390\/rs11212484"},{"key":"ref_21","unstructured":"Ford, B.L.R., and Fulkerson, D.R. (1962). Flows in Networks, Princeton University Press. Mathematics of Computation."},{"key":"ref_22","first-page":"1767","article-title":"A Review of Cooperative Image Segmentation Methods","volume":"29","author":"Ma","year":"2017","journal-title":"J. Comput. Aided Des. Graph."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Listner, C., and Niemeyer, I. (2011, January 24\u201329). Recent advances in object-based change detection. Proceedings of the Geoscience & Remote Sensing Symposium, Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6048910"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lefebvre, A., Corpetti, T., and Hubertmoy, L. (2009, January 12\u201317). Object-Oriented Approach and Texture Analysis for Change Detection in Very High Resolution Images. Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2008.4779809"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"112901","DOI":"10.1016\/j.eswa.2019.112901","article-title":"A Comprehensive Overview of Relevant Methods of Image Cosegmentation","volume":"140","author":"Merdassi","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_26","first-page":"1039","article-title":"High-resolution remote sensing image change detection based on collaborative segmentation","volume":"51","author":"Yuan","year":"2015","journal-title":"J. Nanjing Univ."},{"key":"ref_27","unstructured":"Xie, Z.L. (2017). Remote Sensing Image Change Detection Based on Collaborative Segmentation. [Master\u2019s Thesis, Beijing Construction University]. (In Chinese)."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"597","DOI":"10.14358\/PERS.85.8.597","article-title":"Exploiting Cosegmentation and Geo-Eco Zoning for Land Cover Product Updating","volume":"85","author":"Zhu","year":"2019","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_29","first-page":"10","article-title":"Learning a classification model for segmentation","volume":"2","author":"Ren","year":"2003","journal-title":"IEEE Comput. Soc."},{"key":"ref_30","unstructured":"Shi, J., and Malik, J. (1997, January 17\u201319). Normalized cuts and image segmentation. Proceedings of the Conference on Computer Vision & Pattern Recognition, Washington, DC, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Moore, A.P., Prince, S.J.D., Warrell, J., Mohammed, U., and Jones, G. (2008, January 24\u201326). Superpixel lattices. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587471"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1007\/s11263-014-0744-2","article-title":"SEEDS: Superpixels Extracted Via Energy-Driven Sampling","volume":"111","author":"Bergh","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2017.03.007","article-title":"Superpixels: An evaluation of the state-of-the-art","volume":"166","author":"Stutz","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yao, J., Boben, M., Fidler, S., and Urtasun, R. (2015, January 7\u201312). Real-time coarse-to-ne topologically preserving segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298913"},{"key":"ref_35","unstructured":"Lui, M.Y., Tuzel, O., Ramalingam, S., and Chellappa, R. (2011, January 20\u201325). Entropy rate superpixel segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Springs, CO, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mester, R., Conrad, C., and Guevara, A. (2011, January 23\u201325). Multichannel segmentation using contour relaxation: Fast super-pixels and temporal propagation. Proceedings of the Scandinavian Conference Image Analysis, Ystad, Sweden.","DOI":"10.1007\/978-3-642-21227-7_24"},{"key":"ref_37","first-page":"20","article-title":"Eikonal based region growing for superpixels generation: Application to semi-supervised real time organ segmentation in CT images","volume":"35","author":"Buyssens","year":"2014","journal-title":"Innov. Res. Biomed. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-020-05859-w","article-title":"Satellite imagery and spectral matching for improved estimation of calcium carbonate and iron oxide abundance in mine areas","volume":"13","author":"Srinivasaperumal","year":"2020","journal-title":"Arab. J. Geosci."},{"key":"ref_39","first-page":"754","article-title":"Algorithm for solution of a problem of maximum flow in networks with power estimation","volume":"11","author":"Dinic","year":"1970","journal-title":"Sov. Math Dokl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"804","DOI":"10.3390\/rs9080804","article-title":"Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images","volume":"9","author":"Biao","year":"2017","journal-title":"Remote Sens."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/2\/94\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:26:49Z","timestamp":1760160409000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/2\/94"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,23]]},"references-count":40,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["info12020094"],"URL":"https:\/\/doi.org\/10.3390\/info12020094","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,23]]}}}