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dc.contributor.authorHarrou, Fouzi
dc.contributor.authorZerrouki, Nabil
dc.contributor.authorSun, Ying
dc.contributor.authorHocini, Lotfi
dc.date.accessioned2019-03-20T13:22:23Z
dc.date.available2019-03-20T13:22:23Z
dc.date.issued2019-02-28
dc.identifier.citationHarrou F, Zerrouki N, Sun Y, Hocini L (2018) Monitoring land-cover changes by combining a detection step with a classification step. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). Available: http://dx.doi.org/10.1109/SSCI.2018.8628774.
dc.identifier.doi10.1109/SSCI.2018.8628774
dc.identifier.urihttp://hdl.handle.net/10754/631693
dc.description.abstractAn approach merging the HotellingT 2 control scheme with weighted random forest classifier is proposed and used in the context of detecting land cover changes via remote sensing and radiometric measurements. HotellingT 2 procedure is introduced to identify features corresponding to changed areas. However, 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 unbalanced problems, has been successfully applied on features of the detected pixels to recognize the type of change. The performance of the algorithm is evaluated using SZTAKI AirChange benchmark data, results show that the proposed detection scheme succeeds to appropriately identify changes to land cover. Also, we compared the proposed approach to that of the conventional algorithms (i.e., neural network, random forest, support vector machine and k-nearest neighbors) and found improved performance.
dc.description.sponsorshipThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. The authors (Nabil Zerrouki and Lotfi H Hocini) would like to thank the DIIM laboratory, Centre de Developpement des Technologies Avancees (CDTA) for the continued support during the research.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8628774
dc.rightsArchived with thanks to 2018 IEEE Symposium Series on Computational Intelligence (SSCI)
dc.subjectLand cover change detection
dc.subjectmulti-date measurements
dc.subjectmulti-spectral sensors
dc.subjectmultivariate statistical approach
dc.subjectRandom Forest classification
dc.subjectremote sensing
dc.titleMonitoring land-cover changes by combining a detection step with a classification step
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEnvironmental Statistics Group
dc.contributor.departmentStatistics Program
dc.identifier.journal2018 IEEE Symposium Series on Computational Intelligence (SSCI)
dc.conference.date2018-11-18 to 2018-11-21
dc.conference.name8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
dc.conference.locationBangalore, IND
dc.eprint.versionPost-print
dc.contributor.institutionDIIM Laboratory, Center for Development of Advanced Technology (CDTA), Baba-Hassen, Algiers, , Algeria
dc.contributor.institutionFaculty of Electronics and Computer Science, University of Sciences and Technology Houari Boumédienne, LCPTS, Algiers, , Algeria
dc.contributor.institutionMouloud Mammeri University (UMMTO), Departement of Electronics, Faculty of Electronic Engineering and Computer Science, Laboratory of Analysis and Modeling of the Random Phenomena, BP 17 RP, Tizi-Ouzou, , Algeria
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberOSR-2015-CRG4-2582
refterms.dateFOA2019-03-20T13:23:53Z
dc.date.published-online2019-02-28
dc.date.published-print2018-11


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