dc.contributor.author Zerrouki, Nabil dc.contributor.author Harrou, Fouzi dc.contributor.author Sun, Ying dc.contributor.author Hocini, Lotfi dc.date.accessioned 2019-07-04T11:25:48Z dc.date.available 2019-07-04T11:25:48Z dc.date.issued 2019-07-15 dc.identifier.citation Zerrouki, N., Harrou, F., Sun, Y., & Hocini, L. (2019). A Machine Learning-Based Approach for Land Cover Change Detection Using Remote Sensing and Radiometric Measurements. IEEE Sensors Journal, 19(14), 5843–5850. doi:10.1109/jsen.2019.2904137 dc.identifier.doi 10.1109/JSEN.2019.2904137 dc.identifier.uri http://hdl.handle.net/10754/655916 dc.description.abstract An 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.sponsorship This 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.publisher Institute of Electrical and Electronics Engineers (IEEE) dc.relation.url https://ieeexplore.ieee.org/document/8664182/ dc.relation.url https://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.subject Land cover change detection dc.subject multi-spectral sensors dc.subject multi-date measurements dc.subject remote sensing dc.subject multivariate statistical approach dc.subject random forest classification dc.title A Machine Learning-Based Approach for Land Cover Change Detection Using Remote Sensing and Radiometric Measurements dc.type Article dc.contributor.department Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division dc.contributor.department Statistics dc.contributor.department Statistics Program dc.identifier.journal IEEE Sensors Journal dc.eprint.version Post-print dc.contributor.institution LCPTS Faculty of Electronics and Computer Science, University of Sciences and Technology Houari Boumédienne, Algiers, Algeria dc.contributor.institution DIIM Laboratory, Center for Development of Advanced Technology (CDTA), Algiers, Algeria kaust.person Harrou, Fouzi kaust.person Sun, Ying kaust.grant.number OSR-2015-CRG4-2582 refterms.dateFOA 2019-07-07T11:54:23Z
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