Deep learning-based super-resolution for harmful algal bloom monitoring of inland water

Inland water frequently occurs during harmful algal blooms (HABs), rendering it challenging to comprehend the spatiotemporal features of algal dynamics. Recently, remote sensing has been applied to effectively detect the algal spatiotemporal behaviors in expensive water bodies. However, image sensor...

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Detalhes bibliográficos

Principais autores:	Do Hyuck Kwon, Seok Min Hong, Ather Abbas, Sanghyun Park, Gibeom Nam, Jae-Hyun Yoo, Kyunghyun Kim, Hong Tae Kim, JongCheol Pyo, Kyung Hwa Cho
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Taylor & Francis Group 2023-12-01
Colecção:	GIScience & Remote Sensing
Assuntos:	super-resolution convolutional neural network (cnn) generative adversarial network (gan) remote sensing chlorophyll-a
Acesso em linha:	http://dx.doi.org/10.1080/15481603.2023.2249753
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