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Comparison of two classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia

Deilmai, B. Rokni and Ahmad, Baharin and Zabihi, Hasan (2014) Comparison of two classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia. IOP Conference Series: Earth and Environmental Science, 20 (1). ISSN 1755-1307


Official URL: http://dx.doi.org/10.1088/1755-1315/20/1/012052


Mapping is essential for the analysis of the land use and land cover, which influence many environmental processes and properties. For the purpose of the creation of land cover maps, it is important to minimize error. These errors will propagate into later analyses based on these land cover maps. The reliability of land cover maps derived from remotely sensed data depends on an accurate classification. In this study, we have analyzed multispectral data using two different classifiers including Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). To pursue this aim, Landsat Thematic Mapper data and identical field-based training sample datasets in Johor Malaysia used for each classification method, which results indicate in five land cover classes forest, oil palm, urban area, water, rubber. Classification results indicate that SVM was more accurate than MLC. With demonstrated capability to produce reliable cover results, the SVM methods should be especially useful for land cover classification

Item Type:Article
Uncontrolled Keywords:land use, maximum likelihood estimation, palm oil, remote sensing, support vector machines
Subjects:H Social Sciences > HD Industries. Land use. Labor
Divisions:Geoinformation and Real Estate
ID Code:52187
Deposited By: Siti Nor Hashidah Zakaria
Deposited On:01 Feb 2016 11:52
Last Modified:17 Sep 2018 12:01

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