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

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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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:GIScience & Remote Sensing
Subjects:
super-resolution
convolutional neural network (cnn)
generative adversarial network (gan)
remote sensing
chlorophyll-a
Online Access:http://dx.doi.org/10.1080/15481603.2023.2249753

Internet

http://dx.doi.org/10.1080/15481603.2023.2249753

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