Monitoring land-cover changes by combining a detection step with a classification step
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionEnvironmental Statistics Group
Statistics Program
KAUST Grant Number
OSR-2015-CRG4-2582Date
2019-02-28Online Publication Date
2019-02-28Print Publication Date
2018-11Permanent link to this record
http://hdl.handle.net/10754/631693
Metadata
Show full item recordAbstract
An 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.Citation
Harrou 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.Sponsors
This 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 name
8th IEEE Symposium Series on Computational Intelligence, SSCI 2018Additional Links
https://ieeexplore.ieee.org/document/8628774ae974a485f413a2113503eed53cd6c53
10.1109/SSCI.2018.8628774