Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery

Seok Min Honga Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Republic of Korea

Kyung Hwa Choa Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Republic of Korea

Sanghyun Parkb Water Quality Assessment Research Division, National Institute of Environmental Research, Republic of Korea

Taegu Kangc Han River Environment Research Center, National Institute of Environmental Research, Incheon, Republic of Korea

Moon Sung Kimd Environmental Microbial and Food Safety Laboratory, USDA Agricultural Research Service, Beltsville, MD, USA

Gibeom Namb Water Quality Assessment Research Division, National Institute of Environmental Research, Republic of KoreaCorrespondence[email protected]

JongCheol Pyoe Center for Environmental Data Strategy, Korea Environment Institute, Sejong, Republic of KoreaCorrespondence[email protected]

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Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery

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Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery

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