SAR remote sensing for monitoring harmful algal blooms using deep learning models

SAR remote sensing for monitoring harmful algal blooms using deep learning models

Harmful Algal Blooms (HABs) threaten aquatic ecosystems, necessitating effective monitoring strategies in water resource management. Satellite-based remote sensing has emerged as a popular method to address the limitations of in-situ monitoring. However, cloud covers can obstruct optical imagery, ca...

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Bibliographic Details
Main Authors: Kritnipit Phetanan, Do Hyuck Kwon, Jinmyeong Lee, Heewon Jeong, Gibeom Nam, Euiho Hwang, JongCheol Pyo, Kyung Hwa Cho
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:GIScience & Remote Sensing
Subjects:
Harmful algal blooms
remote sensing
synthetic aperture radar
deep learning
object detection
faster regional convolutional neural network
Online Access:https://www.tandfonline.com/doi/10.1080/15481603.2025.2524202
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https://www.tandfonline.com/doi/10.1080/15481603.2025.2524202

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