Article
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Gaussian Process and Deep Learning Atmospheric Correction
Version 1
: Received: 20 October 2022 / Approved: 1 November 2022 / Online: 1 November 2022 (03:47:57 CET)
A peer-reviewed article of this Preprint also exists.
Basener, B.; Basener, A. Gaussian Process and Deep Learning Atmospheric Correction. Remote Sens. 2023, 15, 649. Basener, B.; Basener, A. Gaussian Process and Deep Learning Atmospheric Correction. Remote Sens. 2023, 15, 649.
Abstract
Atmospheric correction is the processes of converting radiance measured at a spectral 1 sensor to the reflectance of the materials in a multispectral or hyperspectral image. This is an 2 important step for detecting or identifying the materials present in the pixel spectra. We present 3 two machine learning models for atmospheric correction trained and tested on 100,000 batches of 40 4 reflectance spectra converted to radiance using MODTRAN, so the machine learning model learns 5 the radiative transfer physics from MODTRAN. We created a theoretically interpretable Bayesian 6 Gaussian process model and a deep learning autoencoder treating the atmosphere as noise. We 7 compare both methods for estimating gain in the correction model to the well-know QUAC method 8 of assuming a constant mean endmember reflectance. Prediction of reflectance using the Gaussian 9 process model outperforms the other methods in terms of both accuracy and reliability.
Keywords
atmospheric compensation; Gaussian process; hyperspectral
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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