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http://dx.doi.org/10.7319/kogsis.2017.25.1.009

Accuracy Evaluation of Supervised Classification by Using Morphological Attribute Profiles and Additional Band of Hyperspectral Imagery  

Park, Hong Lyun (Department of Civil Engineering, Chungbuk National University)
Choi, Jae Wan (School of Civil Engineering, Chungbuk National University)
Publication Information
Journal of Korean Society for Geospatial Information Science / v.25, no.1, 2017 , pp. 9-17 More about this Journal
Abstract
Hyperspectral imagery is used in the land cover classification with the principle component analysis and minimum noise fraction to reduce the data dimensionality and noise. Recently, studies on the supervised classification using various features having spectral information and spatial characteristic have been carried out. In this study, principle component bands and normalized difference vegetation index(NDVI) was utilized in the supervised classification for the land cover classification. To utilize additional information not included in the principle component bands by the hyperspectral imagery, we tried to increase the classification accuracy by using the NDVI. In addition, the extended attribute profiles(EAP) generated using the morphological filter was used as the input data. The random forest algorithm, which is one of the representative supervised classification, was used. The classification accuracy according to the application of various features based on EAP was compared. Two areas was selected in the experiments, and the quantitative evaluation was performed by using reference data. The classification accuracy of the proposed algorithm showed the highest classification accuracy of 85.72% and 91.14% compared with existing algorithms. Further research will need to develop a supervised classification algorithm and additional input datasets to improve the accuracy of land cover classification using hyperspectral imagery.
Keywords
Hyperspectral Imagery; Principle Component Analysis; Random Forest; Morphological Filter; Extended Attribute Profiles;
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