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dc.contributor.authorZerrouki, Nabil
dc.contributor.authorHarrou, Fouzi
dc.contributor.authorSun, Ying
dc.contributor.authorHocini, Lotfi
dc.date.accessioned2019-07-04T11:25:48Z
dc.date.available2019-07-04T11:25:48Z
dc.date.issued2019-07-15
dc.identifier.doi10.1109/JSEN.2019.2904137
dc.identifier.urihttp://hdl.handle.net/10754/655916
dc.description.abstractAn approach combining the Hotelling $T^{2}$ control method with a weighted random forest classifier is proposed and used in the context of detecting land cover changes via remote sensing and radiometric measurements. Hotelling $T^{2}$ procedure is introduced to identify features corresponding to changed areas. Nevertheless, $T^{2}$ scheme is not able to separate real from false changes. To tackle this limitation, the weighted random forest algorithm, which is an efficient classification technique for imbalanced problems, has been successfully applied to the features of the detected pixels to recognize the type of change. The feasibility of the proposed procedure is verified using SZTAKI AirChange benchmark data. Results proclaim that the proposed detection scheme succeeds to effectively identify land cover changes. Also, the comparisons with other methods (i.e., neural network, random forest, support vector machine, and $k$ -nearest neighbors) highlight the superiority of the proposed method.
dc.description.sponsorshipThis work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award OSR-2015-CRG4-2582.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8664182/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8664182
dc.rights(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectLand cover change detection
dc.subjectmulti-spectral sensors
dc.subjectmulti-date measurements
dc.subjectremote sensing
dc.subjectmultivariate statistical approach
dc.subjectrandom forest classification
dc.titleA Machine Learning-Based Approach for Land Cover Change Detection Using Remote Sensing and Radiometric Measurements
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering
dc.contributor.departmentStatistics
dc.identifier.journalIEEE Sensors Journal
dc.eprint.versionPost-print
dc.contributor.institutionLCPTS Faculty of Electronics and Computer Science, University of Sciences and Technology Houari Boumédienne, Algiers, Algeria
dc.contributor.institutionDIIM Laboratory, Center for Development of Advanced Technology (CDTA), Algiers, Algeria
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberOSR-2015-CRG4-2582
refterms.dateFOA2019-07-07T11:54:23Z


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