HABNet: Machine Learning, Remote Sensing-Based Detection of Harmful Algal Blooms

HABNet: Machine Learning, Remote Sensing-Based Detection of Harmful Algal Blooms

This article describes the application of machine learning techniques to develop state-of-the-art detection and prediction system for spatiotemporal events found within remote sensing data; specifically, harmful algal bloom (HAB) events. We propose HAB detection system based on a ground truth histor...

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Bibliographic Details
Main Authors: Paul R. Hill, Anurag Kumar, Marouane Temimi, David R. Bull
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Convolutional neural networks (CNNs)
deep learning
harmful algal blooms (HABs)
long short-term memory (LSTMs)
random forest (RF)
support vector machine (SVM)
Online Access:https://ieeexplore.ieee.org/document/9113293/

Internet

https://ieeexplore.ieee.org/document/9113293/

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