A Machine Learning-Based Approach for Land Cover Change Detection Using Remote Sensing and Radiometric Measurements
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics
Statistics Program
KAUST Grant Number
OSR-2015-CRG4-2582Date
2019-07-15Permanent link to this record
http://hdl.handle.net/10754/655916
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Show full item recordAbstract
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.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.2019.2904137Sponsors
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.Journal
IEEE Sensors JournalAdditional Links
https://ieeexplore.ieee.org/document/8664182/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8664182
ae974a485f413a2113503eed53cd6c53
10.1109/JSEN.2019.2904137