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http://dx.doi.org/10.11108/kagis.2021.24.1.001

Detection of Surface Water Bodies in Daegu Using Various Water Indices and Machine Learning Technique Based on the Landsat-8 Satellite Image  

CHOUNG, Yun-Jae (Geospatial Research Center, GEO C&I., Ltd.)
KIM, Kyoung-Seop (Geospatial Research Center, GEO C&I., Ltd.)
PARK, In-Sun (Geospatial Research Center, GEO C&I., Ltd.)
CHUNG, Youn-In (Dept. of Civil Engineering, Keimyung University)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.24, no.1, 2021 , pp. 1-11 More about this Journal
Abstract
Detection of surface water features including river, wetland, reservoir from the satellite imagery can be utilized for sustainable management and survey of water resources. This research compared the water indices derived from the multispectral bands and the machine learning technique for detecting the surface water features from he Landsat-8 satellite image acquired in Daegu through the following steps. First, the NDWI(Normalized Difference Water Index) image and the MNDWI(Modified Normalized Difference Water Index) image were separately generated using the multispectral bands of the given Landsat-8 satellite image, and the two binary images were generated from these NDWI and MNDWI images, respectively. Then SVM(Support Vector Machine), the widely used machine learning techniques, were employed to generate the land cover image and the binary image was also generated from the generated land cover image. Finally the error matrices were used for measuring the accuracy of the three binary images for detecting the surface water features. The statistical results showed that the binary image generated from the MNDWI image(84%) had the relatively low accuracy than the binary image generated from the NDWI image(94%) and generated by SVM(96%). And some misclassification errors occurred in all three binary images where the land features were misclassified as the surface water features because of the shadow effects.
Keywords
Landsat-8 Satellite Image; Surface Water; Normalized Difference Water Index(NDWI); Modified Normalized Difference Water Index(MNDWI); Machine Learning; Support Vector Machine(SVM);
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Times Cited By KSCI : 1  (Citation Analysis)
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