Monitoring land-cover changes by combining a detection step with a classification step
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2015-CRG4-2582
Online Publication Date2019-02-28
Print Publication Date2018-11
Permanent link to this recordhttp://hdl.handle.net/10754/631693
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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.
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.
SponsorsThis 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.
Conference/Event name8th IEEE Symposium Series on Computational Intelligence, SSCI 2018