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Remote Sensing
Volume 13
Issue 24
10.3390/rs13245061
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Article

An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing

by
Adrian Doicu
1,*,
Alexandru Doicu
2,
Dmitry S. Efremenko
1,
Diego Loyola
1 and
Thomas Trautmann
1
1
Remote Sensing Technology Institute, German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany
2
Institute of Mathematics, University of Augsburg, 86159 Augsburg, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(24), 5061; https://doi.org/10.3390/rs13245061
Submission received: 4 November 2021 / Revised: 8 December 2021 / Accepted: 10 December 2021 / Published: 13 December 2021
(This article belongs to the Special Issue Recent Advances in Neural Network for Remote Sensing)
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Abstract

In this paper, we present neural network methods for predicting uncertainty in atmospheric remote sensing. These include methods for solving the direct and the inverse problem in a Bayesian framework. In the first case, a method based on a neural network for simulating the radiative transfer model and a Bayesian approach for solving the inverse problem is proposed. In the second case, (i) a neural network, in which the output is the convolution of the output for a noise-free input with the input noise distribution; and (ii) a Bayesian deep learning framework that predicts input aleatoric and model uncertainties, are designed. In addition, a neural network that uses assumed density filtering and interval arithmetic to compute uncertainty is employed for testing purposes. The accuracy and the precision of the methods are analyzed by considering the retrieval of cloud parameters from radiances measured by the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR).
Keywords: neural networks; interval arithmetic; radiative transfer neural networks; interval arithmetic; radiative transfer
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MDPI and ACS Style

Doicu, A.; Doicu, A.; Efremenko, D.S.; Loyola, D.; Trautmann, T. An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing. Remote Sens. 2021, 13, 5061. https://doi.org/10.3390/rs13245061

AMA Style

Doicu A, Doicu A, Efremenko DS, Loyola D, Trautmann T. An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing. Remote Sensing. 2021; 13(24):5061. https://doi.org/10.3390/rs13245061

Chicago/Turabian Style

Doicu, Adrian, Alexandru Doicu, Dmitry S. Efremenko, Diego Loyola, and Thomas Trautmann. 2021. "An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing" Remote Sensing 13, no. 24: 5061. https://doi.org/10.3390/rs13245061

APA Style

Doicu, A., Doicu, A., Efremenko, D. S., Loyola, D., & Trautmann, T. (2021). An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing. Remote Sensing, 13(24), 5061. https://doi.org/10.3390/rs13245061

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