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http://dx.doi.org/10.7848/ksgpc.2018.36.6.433

Detection of Cropland in Reservoir Area by Using Supervised Classification of UAV Imagery Based on GLCM  

Kim, Gyu Mun (Korea Water Resources Corporation)
Choi, Jae Wan (School of Civil Engineering, Chungbuk National University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.36, no.6, 2018 , pp. 433-442 More about this Journal
Abstract
The reservoir area is defined as the area surrounded by the planned flood level of the dam or the land under the planned flood level of the dam. In this study, supervised classification based on RF (Random Forest), which is a representative machine learning technique, was performed to detect cropland in the reservoir area. In order to classify the cropland in the reservoir area efficiently, the GLCM (Gray Level Co-occurrence Matrix), which is a representative technique to quantify texture information, NDWI (Normalized Difference Water Index) and NDVI (Normalized Difference Vegetation Index) were utilized as additional features during classification process. In particular, we analyzed the effect of texture information according to window size for generating GLCM, and suggested a methodology for detecting croplands in the reservoir area. In the experimental result, the classification result showed that cropland in the reservoir area could be detected by the multispectral, NDVI, NDWI and GLCM images of UAV, efficiently. Especially, the window size of GLCM was an important parameter to increase the classification accuracy.
Keywords
Cropland; Gray Level Co-occurrence Matrix; Random Forest; Reservoir Area;
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Times Cited By KSCI : 5  (Citation Analysis)
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