{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:48:06Z","timestamp":1761598086992,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,6,26]],"date-time":"2019-06-26T00:00:00Z","timestamp":1561507200000},"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>Postclassification Comparison (PCC) has been widely used as a change-detection method. The PCC algorithm is straightforward and easily applicable to all satellite images, regardless of whether they are acquired from the same sensor or in the same environmental conditions. However, PCC is prone to cumulative error, which results from classification errors. Alternatively, Change Vector Analysis in Posterior Probability Space (CVAPS), which interprets change based on comparing the posterior probability vectors of a pixel, can alleviate the classification error accumulation present in PCC. CVAPS identifies the type of change based on the direction of a change vector. However, a change vector can be translated to a new position within the feature space; consequently, it is not inconceivable that identical measures of direction may be used by CVAPS to describe multiple types of change. Our proposed method identifies land-cover transitions by using a fusion of CVAPS and PCC. In the proposed algorithm, contrary to CVAPS, a threshold does not need to be specified in order to extract change. Moreover, the proposed method uses a Random Forest as a trainable fusion method in order to obtain a change map directly in a feature space which is obtained from CVAPS and PCC. In other words, there is no need to specify a threshold to obtain a change map through the CVAPS method and then combine it with the change map obtained from the PCC method. This is an advantage over other change-detection methods focused on fusing multiple change-detection approaches. In addition, the proposed method identifies different types of land-cover transitions, based on the fusion of CVAPS and PCC, to improve the results of change-type determination. The proposed method is applied to images acquired by Landsat and Quickbird. The resultant maps confirm the utility of the proposed method as a change-detection\/labeling tool. For example, the new method has an overall accuracy and a kappa coefficient relative improvement of 7% and 9%, respectively, on average, over CVAPS and PCC in determining different types of change.<\/jats:p>","DOI":"10.3390\/rs11131511","type":"journal-article","created":{"date-parts":[[2019,6,26]],"date-time":"2019-06-26T07:24:17Z","timestamp":1561533857000},"page":"1511","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Fusion of Change Vector Analysis in Posterior Probability Space and Postclassification Comparison for Change Detection from Multispectral Remote Sensing Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6797-1512","authenticated-orcid":false,"given":"Fatemeh","family":"Zakeri","sequence":"first","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1439957131, Iran"}]},{"given":"Bo","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong"},{"name":"Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong"}]},{"given":"Mohammad Reza","family":"Saradjian","sequence":"additional","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1439957131, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1080\/10106049209354378","article-title":"Land use and land cover change detection with Landsat MSS and SPOT HRV data in Hong Kong","volume":"7","author":"Fung","year":"1992","journal-title":"Geocarto Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2012.05.006","article-title":"An automated approach for updating land cover maps based on integrated change detection and classification methods","volume":"71","author":"Chen","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1080\/10106049.2016.1195882","article-title":"A land-cover change detection method using data-oriented composite-kernel-based one-class support vector machine","volume":"32","author":"Zhao","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3047","DOI":"10.1080\/01431160500057889","article-title":"Spatial knowledge databases as applied to the detection of changes in urban land use","volume":"26","author":"Chou","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1109\/LGRS.2010.2068537","article-title":"Change vector analysis in posterior probability space: A new method for land cover change detection","volume":"8","author":"Chen","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.3390\/rs3112473","article-title":"A new approach to change vector analysis using distance and similarity measures","volume":"3","author":"Gillespie","year":"2011","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1007\/s12046-014-0286-x","article-title":"A comparative study on change vector analysis based change detection techniques","volume":"39","author":"Singh","year":"2014","journal-title":"Sadhana"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.01.006","article-title":"A critical synthesis of remotely sensed optical image change detection techniques","volume":"160","author":"Tewkesbury","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1109\/TGRS.2008.916089","article-title":"Classifying multilevel imagery from SAR and optical sensors by decision fusion","volume":"46","author":"Waske","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.inffus.2012.05.003","article-title":"Information fusion techniques for change detection from multi-temporal remote sensing images","volume":"14","author":"Du","year":"2013","journal-title":"Inf. Fusion"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1109\/LGRS.2013.2275738","article-title":"Using combined difference image and k-means clustering for SAR image change detection","volume":"11","author":"Zheng","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3405","DOI":"10.1109\/JSTARS.2015.2508043","article-title":"Remote-sensing image change detection with fusion of multiple wavelet kernels","volume":"9","author":"Jia","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","first-page":"345","article-title":"Unsupervised change detection in remote sensing images using fusion of spectral and statistical indices","volume":"21","author":"Singh","year":"2018","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Luo, H., Liu, C., Wu, C., and Guo, X. (2018). Urban change detection based on Dempster\u2013Shafer theory for multitemporal very high-resolution imagery. Remote Sens., 10.","DOI":"10.3390\/rs10070980"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.isprsjprs.2009.01.003","article-title":"Classifier ensembles for land cover mapping using multitemporal SAR imagery","volume":"64","author":"Waske","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2723","DOI":"10.1080\/014311699211769","article-title":"Detection of partial land cover change associated with the migration of inter-class transitional zones","volume":"20","author":"Foody","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.rse.2017.12.025","article-title":"Detecting Himalayan glacial lake outburst floods from Landsat time series","volume":"207","author":"Veh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1111\/j.2517-6161.1986.tb01412.x","article-title":"On the statistical analysis of dirty pictures","volume":"48","author":"Besag","year":"1986","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kuncheva, L.I. (2004). Combining Pattern Classifiers: Methods and Algorithms, John Wiley & Sons.","DOI":"10.1002\/0471660264"},{"key":"ref_21","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\/13\/1511\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:01:23Z","timestamp":1760187683000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1511"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,26]]},"references-count":21,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11131511"],"URL":"https:\/\/doi.org\/10.3390\/rs11131511","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,6,26]]}}}