Hyperspectral retrievals of suspended sediment using cluster-based machine learning regression in shallow waters.

Kwon, Siyoon; Seo, Il Won; Noh, Hyoseob; Kim, Byunguk

Kwon, Siyoon; Seo, Il Won; Noh, Hyoseob; Kim, Byunguk.

Afiliação

Kwon S; Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Republic of Korea. Electronic address: ksy92@snu.ac.kr.
Seo IW; Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Republic of Korea. Electronic address: seoilwon@snu.ac.kr.
Noh H; Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Republic of Korea. Electronic address: hyoddubi1@snu.ac.kr.
Kim B; Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Republic of Korea. Electronic address: kim.byunguk@snu.ac.kr.

Sci Total Environ ; 833: 155168, 2022 Aug 10.

Article em En | MEDLINE | ID: mdl-35417723

Assuntos

Aprendizado de Máquina; Água; Sedimentos Geológicos

Palavras-chave

Gaussian mixture model; Optical variability; Random forest regression; Spatiotemporal distribution; Suspended sediment; UAV-based hyperspectral imagery

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Água / Aprendizado de Máquina Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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