{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T10:21:29Z","timestamp":1777630889767,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2017,8,5]],"date-time":"2017-08-05T00:00:00Z","timestamp":1501891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD) method\u2014a measure for the spectral distortion of change information\u2014is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation.<\/jats:p>","DOI":"10.3390\/rs9080804","type":"journal-article","created":{"date-parts":[[2017,8,9]],"date-time":"2017-08-09T06:32:14Z","timestamp":1502260334000},"page":"804","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3594-7953","authenticated-orcid":false,"given":"Biao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, Anhui, China"},{"name":"Anhui Key Laboratory of Smart City and Geographical Condition Monitoring, Hefei 230031, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaewan","family":"Choi","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Chungbuk National University, Cheongju 361763, Chungbuk, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seokeun","family":"Choi","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Chungbuk National University, Cheongju 361763, Chungbuk, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8755-7711","authenticated-orcid":false,"given":"Soungki","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Chungbuk National University, Cheongju 361763, Chungbuk, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1983-5978","authenticated-orcid":false,"given":"Penghai","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5976","DOI":"10.3390\/rs6075976","article-title":"Change detection algorithm for the production of land cover change maps over the european union countries","volume":"6","author":"Aleksandrowicz","year":"2014","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.rse.2011.10.030","article-title":"Continuous monitoring of forest disturbance using all available landsat imagery","volume":"122","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2013.08.026","article-title":"Using atmospherically-corrected landsat imagery to measure glacier area change in the cordillera blanca, peru from 1987 to 2010","volume":"140","author":"Burns","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.12.027","article-title":"Characterizing the magnitude, timing and duration of urban growth from time series of landsat-based estimates of impervious cover","volume":"175","author":"Song","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1016\/j.rse.2011.01.022","article-title":"Mapping wildfire and clearcut harvest disturbances in boreal forests with landsat time series data","volume":"115","author":"Schroeder","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.rse.2017.04.021","article-title":"A land cover change detection and classification protocol for updating alaska nlcd 2001 to 2011","volume":"195","author":"Jin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.enggeo.2013.07.014","article-title":"An integrated approach for the prediction of subsidence for coal mining basins","volume":"166","author":"Unlu","year":"2013","journal-title":"Eng. Geol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.coal.2010.11.012","article-title":"Evaluation of pollution levels at an abandoned coal mine site in turkey with the aid of gis","volume":"86","author":"Yenilmez","year":"2011","journal-title":"Int. J. Coal Geol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.coal.2011.01.005","article-title":"Analyses and monitoring of lignite mining lakes in eastern germany with spectral signatures of landsat tm satellite data","volume":"86","author":"Schroeter","year":"2011","journal-title":"Int. J. Coal Geol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.rse.2017.03.037","article-title":"Analysing land cover and land use change in the matobo national park and surroundings in zimbabwe","volume":"194","author":"Scharsich","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2016.12.008","article-title":"Multi-source remotely sensed data fusion for improving land cover classification","volume":"124","author":"Chen","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.rse.2010.08.008","article-title":"Potential of small-baseline sar interferometry for monitoring land subsidence related to underground coal fires: Wuda (Northern China) case study","volume":"115","author":"Jiang","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.rse.2013.08.016","article-title":"Combined use of space-borne sar interferometric techniques and ground-based measurements on a 0.3 km2 subsidence phenomenon","volume":"139","author":"Raucoules","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.isprsjprs.2017.02.009","article-title":"The potential of more accurate insar covariance matrix estimation for land cover mapping","volume":"126","author":"Jiang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/LGRS.2012.2193372","article-title":"A new approach to change detection in multispectral images by means of ergas index","volume":"10","author":"Renza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"10347","DOI":"10.3390\/rs70810347","article-title":"Image fusion-based change detection for flood extent extraction using bi-temporal very high-resolution satellite images","volume":"7","author":"Byun","year":"2015","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(97)00162-4","article-title":"Multivariate alteration detection (mad) and maf postprocessing in multispectral, bitemporal image data: New approaches to change detection studies","volume":"64","author":"Nielsen","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, Q., and Chen, Y. (2016). Multi-feature object-based change detection using self-adaptive weight change vector analysis. Remote Sens., 8.","DOI":"10.3390\/rs8070549"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1080\/2150704X.2015.1062155","article-title":"Application of ir-mad using synthetically fused images for change detection in hyperspectral data","volume":"6","author":"Wang","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TIP.2006.888195","article-title":"The regularized iteratively reweighted mad method for change detection in multi-and hyperspectral data","volume":"16","author":"Nielsen","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1508","DOI":"10.3390\/rs2061508","article-title":"Change detection accuracy and image properties: A study using simulated data","volume":"2","author":"Almutairi","year":"2010","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.inffus.2016.12.007","article-title":"Statistical comparison of image fusion algorithms: Recommendations","volume":"36","author":"Liu","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/S0924-2716(03)00014-5","article-title":"Fusion of hyperspectral and radar data using the ihs transformation to enhance urban surface features","volume":"58","author":"Chen","year":"2003","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1109\/TGRS.2010.2087029","article-title":"An ihs-based fusion for color distortion reduction and vegetation enhancement in ikonos imagery","volume":"49","author":"Taleb","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1109\/JSTSP.2011.2104938","article-title":"A theoretical analysis of the effects of aliasing and misregistration on pansharpened imagery","volume":"5","author":"Baronti","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_27","unstructured":"Laben, C.A., and Brower, B.V. (2000). Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pan-Sharpening. (6011875 A), U.S. Patents."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-017-2878-3","article-title":"Comparison of various pan-sharpening methods using quickbird-2 and landsat-8 imagery","volume":"10","author":"Pushparaj","year":"2017","journal-title":"Arab. J. Geosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.cageo.2006.06.008","article-title":"A general framework for component substitution image fusion: An implementation using the fast image fusion method","volume":"33","author":"Dou","year":"2007","journal-title":"Comput. Geosci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3230","DOI":"10.1109\/TGRS.2007.901007","article-title":"Improving component substitution pansharpening through multivariate regression of ms +pan data","volume":"45","author":"Aiazzi","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.rse.2016.07.028","article-title":"Land cover change detection by integrating object-based data blending model of landsat and modis","volume":"184","author":"Lu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.agrformet.2016.12.006","article-title":"Onset of drying and dormancy in relation to water dynamics of semi-arid grasslands from modis ndwi","volume":"234\u2013235","author":"Ding","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2012.09.009","article-title":"Bci: A biophysical composition index for remote sensing of urban environments","volume":"127","author":"Deng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1109\/TGRS.2007.912448","article-title":"Synthesis of multispectral images to high spatial resolution: A critical review of fusion methods based on remote sensing physics","volume":"46","author":"Thomas","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1109\/LGRS.2011.2109697","article-title":"Improving change detection results of ir-mad by eliminating strong changes","volume":"8","author":"Marpu","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histogram","volume":"9","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1109\/LGRS.2014.2386878","article-title":"Object-based change detection of very high resolution satellite imagery using the cross-sharpening of multitemporal data","volume":"12","author":"Wang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/8\/804\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:41:33Z","timestamp":1760208093000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/8\/804"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,5]]},"references-count":37,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2017,8]]}},"alternative-id":["rs9080804"],"URL":"https:\/\/doi.org\/10.3390\/rs9080804","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,8,5]]}}}