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High-resolution imaging coupled with deep learning model for classifying water body of Soyang Lake, South Korea

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Abstract

Precise monitoring of natural phenomena is essential to reduce potential risk when a disaster occurs. The capability of developing high-resolution satellite images has provided advantages in the Earth’s surface observation. High-resolution images, including KOMPSAT-2/3 and PlanetScope images, have enabled more precise observation of the Earth’s surface. Combining the high-resolution KOMPSAT-2/3 and PlanetScope images and a capable image classifier tool can contribute to improving the confidence level of the observation. Recently, deep learning models provided enhanced capability compared to traditional machine learning, especially in image classification. In this study, we aim to investigate performance comparison between deep learning model using U-Net architecture and traditional machine learning represented by support vector machine (SVM) and random forest (RF) model with a focus on identifying water bodies on a lake. Significant differences in surface area of water of Soyang Lake occurred in 2015 and 2022 was the area of interest of this study. Based on confusion matrix analysis, the best classification results were indicated by deep learning model represented by U-Net with overall accuracy ranging from 97% to 100% and a Kappa coefficient of 0.91–1.00. The two traditional machine learning models, SVM and RF, lower up to 11% in overall accuracy and 0.15 for Kappa coefficient values. We also estimated the surface area of water based on the best classification results and found that the changes in water surface area were correlated with the monthly precipitation with a coefficient correlation of 0.89. This study should give new insight into the classification method for high-resolution satellite images. By providing the precise classification result, it could be contributed to providing proper mitigation and design policy related to natural phenomena on the earth’s surface, especially for monitoring the surface area of the lake water.

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT) (RS-2022-00165154, “Development of Application Support System for Satellite Information Big Data”) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No.2019R1A6A1A03033167) and the research grant of Kangwon National University in 2020.

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Authors and Affiliations

  1. Department of Science Education, Kangwon National University, Chuncheon-si, Gangwon-do, 24341, Republic of Korea

    Suci Ramayanti & Chang-Wook Lee

  2. Department of Smart Regional Innovation, Kangwon National University, Chuncheon-si, Gangwon-do, 24341, Republic of Korea

    Sungjae Park & Chang-Wook Lee

  3. Department of Geophysics, Kangwon National University, 1 Kangwondae-hakgil, Chuncheon-si, Gangwon-do, 24341, Republic of Korea

    Yu-Chul Park

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  1. Suci Ramayanti
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Correspondence to Yu-Chul Park.

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Ramayanti, S., Park, S., Lee, CW. et al. High-resolution imaging coupled with deep learning model for classifying water body of Soyang Lake, South Korea. Geosci J 27, 801–813 (2023). https://doi.org/10.1007/s12303-023-0032-7

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