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http://dx.doi.org/10.11108/kagis.2018.21.3.205

The study on Decision Tree method to improve land cover classification accuracy of Hyperspectral Image  

SEO, Jin-Jae (Korea Land and Geospatial Informatix Corporation)
CHO, Gi-Sung (Dept. of Civil Engineering, Chonbuk National University)
SONG, Jang-Ki (Dept. of Civil Engineering, Chonbuk National University)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.21, no.3, 2018 , pp. 205-213 More about this Journal
Abstract
Hyperspectral image is more increasing spectral resolution that Multi-spectral image. Because of that, each pixel of the hyperspectral image includes much more information and it is considered the most appropriate technic for land cover classification. but recent research of hyperspectral image is stayed land cover classification of general level. therefore we classified land cover of detail level using ED, SAM, SSS method and made Decision Tree from result of that. As a result, the overall accuracy of general level was improved by 1.68% and the overall accuracy of detail level was improved by 5.56%.
Keywords
Hypersepcral Image; SAM(Spectral Angle Mapping); SSS(Spectral Similarity Scale; Land Cover Classification;
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  • Reference
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