On the Potential of Bayesian Neural Networks for Estimating Chlorophyll-a Concentration from Satellite Data

On the Potential of Bayesian Neural Networks for Estimating Chlorophyll-a Concentration from Satellite Data

This work introduces the use of Bayesian Neural Networks (BNNs) for inferring chlorophyll-a concentration ([CHL-a]) from remotely sensed data. BNNs are probabilistic models that associate a probability distribution to the neural network parameters and rely on Bayes’ rule for training. The performanc...

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Bibliographic Details
Main Authors: Mohamad Abed El Rahman Hammoud, Nikolaos Papagiannopoulos, George Krokos, Robert J. W. Brewin, Dionysios E. Raitsos, Omar Knio, Ibrahim Hoteit
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Remote Sensing
Subjects:
ocean color
chlorophyll-a
remote sensing
Bayesian neural network
Online Access:https://www.mdpi.com/2072-4292/17/11/1826
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