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

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Adrian Doicu , Alexandru Doicu , Dmitry Efremenko , Diego Loyola , Thomas Trautmann ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.3390/rs13245061

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).

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Computer Science

Convolutional Neural Network

Remote Sensing

Artificial Intelligence

Machine Learning

Mathematics

Geography

Mathematical Analysis

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Artificial neural network Computer science Convolutional neural network Remote sensing Inverse problem Bayesian probability Noise (video) Uncertainty quantification Convolution (computer science) Artificial intelligence Machine learning Mathematics Image (mathematics) Geography Mathematical analysis

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Type: article
Language: en
Landing Page: https://doi.org/10.3390/rs13245061
PDF: https://www.mdpi.com/2072-4292/13/24/5061/pdf?version=1639544915
OA Status: gold
Cited By: 5
References: 55
Related Works: 10
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DOI: https://doi.org/10.3390/rs13245061

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

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Language: en

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Publication year: 2021

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Publication date: 2021-12-13

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Authors: Adrian Doicu, Alexandru Doicu, Dmitry Efremenko, Diego Loyola, Thomas Trautmann

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Landing page: https://doi.org/10.3390/rs13245061

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Concepts: Artificial neural network, Computer science, Convolutional neural network, Remote sensing, Inverse problem, Bayesian probability, Noise (video), Uncertainty quantification, Convolution (computer science), Artificial intelligence, Machine learning, Mathematics, Image (mathematics), Geography, Mathematical analysis

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Cited by: 5

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