{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T21:01:53Z","timestamp":1761253313001,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,21]],"date-time":"2018-10-21T00:00:00Z","timestamp":1540080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41622107, 41771385"],"award-info":[{"award-number":["41622107, 41771385"]}],"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>Change detection (CD) of natural land cover is important for environmental protection and to maintain an ecological balance. The Landsat series of satellites provide continuous observation of the Earth\u2019s surface and is sensitive to reflection of water, soil and vegetation. It offers fine spatial resolutions (15\u201380 m) and short revisit times (16\u201318 days). Therefore, Landsat imagery is suitable for monitoring natural land cover changes. Clustering-based CD methods using evolutionary algorithms (EAs) can be applied to Landsat images to obtain optimal changed and unchanged clustering centers (clusters) with minimum clustering index. However, they directly analyze difference image (DI), which finds itself subject to interference by Gaussian noise and local brightness distortion in Landsat data, resulting in false alarms in detection results. In order to reduce image interferences and improve CD accuracy, we proposed an unsupervised CD method based on multi-feature clustering using the differential evolution algorithm (M-DECD) for Landsat Imagery. First, according to characteristics of Landsat data, a multi-feature space is constructed with three elements: Wiener de-noising, detail enhancement, and structural similarity. Then, a CD method based on differential evolution (DE) algorithm and fuzzy clustering is proposed to obtain global optimal clusters in the multi-feature space, and generate a binary change map (CM). In addition, the control parameters of the DE algorithm are adjusted to improve the robustness of M-DECD. The experimental results obtained with four Landsat datasets confirm the effectiveness of M-DECD. Compared with the results of conventional methods and the current state-of-the-art methods based on evolutionary clustering, the detection accuracies of the M-DECD on the Mexico dataset and the Sardinia dataset are very close to the best results. The accuracies of the M-DECD in the Alaska dataset and the large Canada dataset increased by about 3.3% and 11.9%, respectively. This indicates that multiple features are suitable for Landsat images and the DE algorithm is effective in searching for an optimal CD result.<\/jats:p>","DOI":"10.3390\/rs10101664","type":"journal-article","created":{"date-parts":[[2018,10,23]],"date-time":"2018-10-23T08:43:36Z","timestamp":1540284216000},"page":"1664","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Change Detection Based on Multi-Feature Clustering Using Differential Evolution for Landsat Imagery"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9444-6761","authenticated-orcid":false,"given":"Mi","family":"Song","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9446-5850","authenticated-orcid":false,"given":"Yanfei","family":"Zhong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ailong","family":"Ma","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Digital change detection techniques using remotely-sensed data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1109\/TIP.2002.999678","article-title":"An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images","volume":"11","author":"Bruzzone","year":"2002","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.1016\/j.asoc.2012.03.060","article-title":"Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images","volume":"12","author":"Mishra","year":"2012","journal-title":"Appl. Soft Comput."},{"key":"ref_4","first-page":"3386","article-title":"Change vector analysis: Detecting of areas associated with flood using landsat TM","volume":"5","author":"Yoon","year":"2003","journal-title":"Int. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1080\/19475705.2014.925982","article-title":"Mapping a burned forest area from landsat tm data by multiple methods","volume":"7","author":"Chen","year":"2016","journal-title":"Geomat. Nat. Hazards Risk."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2011.10.031","article-title":"Detecting post-fire salvage logging from landsat change maps and national fire survey data","volume":"122","author":"Schroeder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rse.2015.03.002","article-title":"Change in the glacier extent in turkey during the landsat ERA","volume":"163","author":"Yavasli","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1109\/LGRS.2016.2645742","article-title":"Change detection in multispectral landsat images using multiobjective evolutionary algorithm","volume":"14","author":"Yavariabdi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"Threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/0031-3203(86)90030-0","article-title":"Minimum error thresholding","volume":"19","author":"Kittler","year":"1986","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1109\/TGRS.2017.2759663","article-title":"A theoretical framework for change detection based on a compound multiclass statistical model of the difference image","volume":"56","author":"Zanetti","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5004","DOI":"10.1109\/TIP.2015.2474710","article-title":"Rayleigh-rice mixture parameter estimation via em algorithm for change detection in multispectral images","volume":"24","author":"Zanetti","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4365","DOI":"10.1080\/01431161.2010.486806","article-title":"Pre-classification and post-classification change-detection techniques to monitor land-cover and land-use change using multi-temporal landsat imagery: A case study on pisa province in italy","volume":"32","author":"Peiman","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/TGRS.2006.888861","article-title":"A context-sensitive technique for unsupervised change detection based on hopfield-type neural networks","volume":"45","author":"Ghosh","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2070","DOI":"10.1109\/TGRS.2008.916643","article-title":"A novel approach to unsupervised change detection based on a semisupervised svm and a similarity measure","volume":"46","author":"Bovolo","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","article-title":"FCM\u2014The fuzzy c-means clustering-algorithm","volume":"10","author":"Bezdek","year":"1984","journal-title":"Comput. Geosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/TIP.2010.2040763","article-title":"A robust fuzzy local information c-means clustering algorithm","volume":"19","author":"Krinidis","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1109\/TIP.2011.2170702","article-title":"Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering","volume":"21","author":"Gong","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/LGRS.2009.2037024","article-title":"Change detection in satellite images using a genetic algorithm approach","volume":"7","author":"Celik","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/j.ins.2010.10.016","article-title":"Fuzzy clustering algorithms for unsupervised change detection in remote sensing images","volume":"181","author":"Ghosh","year":"2011","journal-title":"Inf. Sci."},{"key":"ref_22","first-page":"5910","article-title":"Optimal clustering framework for hyperspectral band selection","volume":"56","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.asoc.2017.11.045","article-title":"Computational intelligence in optical remote sensing image processing","volume":"64","author":"Zhong","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/JSTARS.2013.2240655","article-title":"Automatic fuzzy clustering based on adaptive multi-objective differential evolution for remote sensing imagery","volume":"6","author":"Zhong","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1109\/JSTARS.2014.2303634","article-title":"An adaptive memetic fuzzy clustering algorithm with spatial information for remote sensing imagery","volume":"7","author":"Zhong","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1016\/j.jvcir.2010.09.005","article-title":"Image change detection using gaussian mixture model and genetic algorithm","volume":"21","author":"Celik","year":"2010","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_27","first-page":"941","article-title":"Change detection without difference image computation based on multiobjective cost function optimization","volume":"19","author":"Celik","year":"2011","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/j.asoc.2015.10.044","article-title":"A multiobjective fuzzy clustering method for change detection in sar images","volume":"46","author":"Li","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"063596","DOI":"10.1117\/1.JRS.6.063596","article-title":"Swarm intelligence and fractals in dual-pol synthetic aperture radar image change detection","volume":"6","author":"Aghababaee","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2151","DOI":"10.1109\/JSTARS.2015.2427274","article-title":"Unsupervised change detection in multitemporal multispectral satellite images using parallel particle swarm optimization","volume":"8","author":"Kusetogullari","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Singh, K.K., Mehrotra, A., Nigam, M.J., and Pal, K. (2013, January 12\u201314). Unsupervised change detection from remote sensing images using hybrid genetic FCM. Proceedings of the 2013 Students Conference on Engineering and Systems (Sces): Inspiring Engineering and Systems for Sustainable Development, Allahabad, India.","DOI":"10.1109\/SCES.2013.6547529"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.engappai.2014.02.004","article-title":"Change detection in sar images by artificial immune multi-objective clustering","volume":"31","author":"Shang","year":"2014","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution\u2014A simple and efficient heuristic for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2016.01.004","article-title":"Recent advances in differential evolution\u2014An updated survey","volume":"27","author":"Das","year":"2016","journal-title":"Swarm Evol. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TEVC.2010.2059031","article-title":"Differential evolution: A survey of the state-of-the-art","volume":"15","author":"Das","year":"2011","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/978-3-319-49184-4_36","article-title":"Remote sensing image denoising with iterative adaptive wiener filter","volume":"192","author":"Wang","year":"2017","journal-title":"Springer Proc. Phys."},{"key":"ref_37","unstructured":"Lim, J.S. (1990). Two-Dimensional Signal and Image Processing, Prentice Hall."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Stephane, M., and Charlotte, P. (2015, January 22\u201324). Primal sketch of image series with edge preserving filtering application to change detection. Proceedings of the 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp), Annecy, France.","DOI":"10.1109\/Multi-Temp.2015.7245785"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Moser, G., and Serpico, S.B. (2012). Unsupervised change detection with high-resolution sar images by edge-preserving markov random fields and graph-cuts. Int. Geosci. Remote Sens., 1984\u20131987.","DOI":"10.1109\/IGARSS.2012.6351112"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s00138-007-0107-x","article-title":"Structural similarity-based object tracking in multimodality surveillance videos","volume":"20","author":"Loza","year":"2009","journal-title":"Mach. Vis. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1109\/TSMCB.2012.2189561","article-title":"Remote sensing image subpixel mapping based on adaptive differential evolution","volume":"42","author":"Zhong","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1162\/evco.1993.1.1.25","article-title":"Predictive models for the breeder genetic algorithm I. Continuous parameter optimization","volume":"1","year":"1993","journal-title":"Evol. Comput."},{"key":"ref_44","unstructured":"Dorigo, M. (1992). Optimization, learning and natural algorithms. [PhD Thesis, Politecnico di Milano]."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.tcs.2005.05.020","article-title":"Ant colony optimization theory: A survey","volume":"344","author":"Dorigo","year":"2005","journal-title":"Theor. Comput. Sci."},{"key":"ref_46","unstructured":"Dorigo, M., and Caro, G.D. (1999, January 6\u20139). Ant colony optimization: A new meta-heuristic. Proceedings of the 1999 Congress on Evolutionary Computation (CEC 99), Washington, DC, USA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1664\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:25:16Z","timestamp":1760196316000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1664"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,21]]},"references-count":46,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,10]]}},"alternative-id":["rs10101664"],"URL":"https:\/\/doi.org\/10.3390\/rs10101664","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,10,21]]}}}