KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2015-CRG4-2582
Online Publication Date2018-04-06
Print Publication Date2018-06
Permanent link to this recordhttp://hdl.handle.net/10754/627864
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AbstractAccurate detection of changes in land cover leads to better understanding of the dynamics of landscapes. This letter reports the development of a reliable approach to detecting changes in land cover based on remote sensing and radiometric data. This approach integrates the multivariate exponentially weighted moving average (MEWMA) chart with support vector machines (SVMs) for accurate and reliable detection of changes to land cover. Here, we utilize the MEWMA scheme to identify features corresponding to changed regions. Unfortunately, MEWMA schemes cannot discriminate between real changes and false changes. If a change is detected by the MEWMA algorithm, then we execute the SVM algorithm that is based on features corresponding to detected pixels to identify the type of change. We assess the effectiveness of this approach by using the remote-sensing change detection database and the SZTAKI AirChange benchmark data set. Our results show the capacity of our approach to detect changes to land cover.
CitationZerrouki N, Harrou F, Sun Y (2018) Statistical Monitoring of Changes to Land Cover. IEEE Geoscience and Remote Sensing Letters: 1–5. Available: http://dx.doi.org/10.1109/LGRS.2018.2817522.
SponsorsThis work was supported by the Office of Sponsored Research (OSR), King Abdullah University of Science and Technology under Award OSR-2015-CRG4-2582.