Improved Atmospheric Correction for Remote Imaging Spectroscopy Missions with Accelerated Optimal Estimation Article Swipe
Jouni Susiluoto
,
Niklas Bohn
,
Amy Braverman
,
Philip G. Brodrick
,
Nimrod Carmon
,
Michael R. Gunson
,
Hai Nguyen
,
David R. Thompson
,
Michael Turmon
,
Jouni Susiluoto
,
Niklas Bohn
,
Amy Braverman
,
Philip G. Brodrick
,
Nimrod Carmon
,
Michael R. Gunson
,
Hai Nguyen
,
David R. Thompson
,
Michael Turmon
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17223719
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17223719
Space-based imaging spectrometers that monitor the Earth’s surface generate vast amounts of data, the processing of which requires fast and accurate retrieval algorithms. Estimating scientifically relevant surface properties from remotely measured radiance data typically involves first inferring spectral surface reflectance from the observed radiance, followed by discipline-specific algorithms to derive scientifically relevant properties. Probabilistic reflectance retrieval algorithms, such as the commonly used optimal estimation (OE), are computationally expensive. Furthermore, the Gaussian assumptions associated with OE have not been fully validated in the context of hyperspectral retrievals. To address these challenges, we introduce accelerated optimal estimation (AOE), a Bayesian algorithm that speeds up the OE reflectance inversion process by up to two orders of magnitude compared to a reference OE implementation (ROE), while also providing improved convergence over a number of selected test targets. We also demonstrate that, under given atmospheric conditions, Gaussian uncertainty estimates from OE-type algorithms are accurate. This is achieved by comparing the OE-type posterior distributions to non-Gaussian ones obtained with Markov chain Monte Carlo (MCMC). Finally, we demonstrate how AOE scales to a larger AVIRIS-NG scene, showcasing its ability to handle complex, large-scale data.
Related Topics
Concepts
Hyperspectral imaging
Radiance
Remote sensing
Computer science
Optimal estimation
Reflectivity
Markov chain Monte Carlo
Inversion (geology)
Algorithm
Bayesian probability
Atmospheric correction
Gaussian
Imaging spectroscopy
Context (archaeology)
Gaussian process
Inverse problem
Probabilistic logic
Environmental science
Imaging spectrometer
Monte Carlo method
Remote sensing application
Convergence (economics)
Spectrometer
Statistical model
Bayesian inference
Markov random field
Atmospheric model
Markov chain
Gaussian network model
Estimation theory
Process (computing)
Inverse
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs17223719
- https://www.mdpi.com/2072-4292/17/22/3719/pdf?version=1763132417
- OA Status
- gold
- OpenAlex ID
- https://openalex.org/W7105677633
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7105677633Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs17223719Digital Object Identifier
- Title
-
Improved Atmospheric Correction for Remote Imaging Spectroscopy Missions with Accelerated Optimal EstimationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-14Full publication date if available
- Authors
-
Jouni Susiluoto, Niklas Bohn, Amy Braverman, Philip G. Brodrick, Nimrod Carmon, Michael R. Gunson, Hai Nguyen, David R. Thompson, Michael Turmon, Jouni Susiluoto, Niklas Bohn, Amy Braverman, Philip G. Brodrick, Nimrod Carmon, Michael R. Gunson, Hai Nguyen, David R. Thompson, Michael TurmonList of authors in order
- Landing page
-
https://doi.org/10.3390/rs17223719Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/17/22/3719/pdf?version=1763132417Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-4292/17/22/3719/pdf?version=1763132417Direct OA link when available
- Concepts
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Hyperspectral imaging, Radiance, Remote sensing, Computer science, Optimal estimation, Reflectivity, Markov chain Monte Carlo, Inversion (geology), Algorithm, Bayesian probability, Atmospheric correction, Gaussian, Imaging spectroscopy, Context (archaeology), Gaussian process, Inverse problem, Probabilistic logic, Environmental science, Imaging spectrometer, Monte Carlo method, Remote sensing application, Convergence (economics), Spectrometer, Statistical model, Bayesian inference, Markov random field, Atmospheric model, Markov chain, Gaussian network model, Estimation theory, Process (computing), InverseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.scene, | 178 |
| abstract_inverted_index.speeds | 100 |
| abstract_inverted_index.(MCMC). | 167 |
| abstract_inverted_index.OE-type | 145, 155 |
| abstract_inverted_index.ability | 181 |
| abstract_inverted_index.address | 87 |
| abstract_inverted_index.amounts | 10 |
| abstract_inverted_index.context | 82 |
| abstract_inverted_index.imaging | 1 |
| abstract_inverted_index.monitor | 4 |
| abstract_inverted_index.optimal | 62, 93 |
| abstract_inverted_index.process | 106 |
| abstract_inverted_index.surface | 7, 26, 38 |
| abstract_inverted_index.Bayesian | 97 |
| abstract_inverted_index.Finally, | 168 |
| abstract_inverted_index.Gaussian | 70, 141 |
| abstract_inverted_index.accurate | 20 |
| abstract_inverted_index.achieved | 151 |
| abstract_inverted_index.commonly | 60 |
| abstract_inverted_index.compared | 114 |
| abstract_inverted_index.complex, | 184 |
| abstract_inverted_index.followed | 44 |
| abstract_inverted_index.generate | 8 |
| abstract_inverted_index.improved | 124 |
| abstract_inverted_index.involves | 34 |
| abstract_inverted_index.measured | 30 |
| abstract_inverted_index.observed | 42 |
| abstract_inverted_index.obtained | 161 |
| abstract_inverted_index.radiance | 31 |
| abstract_inverted_index.relevant | 25, 51 |
| abstract_inverted_index.remotely | 29 |
| abstract_inverted_index.requires | 17 |
| abstract_inverted_index.selected | 130 |
| abstract_inverted_index.spectral | 37 |
| abstract_inverted_index.targets. | 132 |
| abstract_inverted_index.AVIRIS-NG | 177 |
| abstract_inverted_index.Earth’s | 6 |
| abstract_inverted_index.accurate. | 148 |
| abstract_inverted_index.algorithm | 98 |
| abstract_inverted_index.comparing | 153 |
| abstract_inverted_index.estimates | 143 |
| abstract_inverted_index.inferring | 36 |
| abstract_inverted_index.introduce | 91 |
| abstract_inverted_index.inversion | 105 |
| abstract_inverted_index.magnitude | 113 |
| abstract_inverted_index.posterior | 156 |
| abstract_inverted_index.providing | 123 |
| abstract_inverted_index.radiance, | 43 |
| abstract_inverted_index.reference | 117 |
| abstract_inverted_index.retrieval | 21, 55 |
| abstract_inverted_index.typically | 33 |
| abstract_inverted_index.validated | 79 |
| abstract_inverted_index.Estimating | 23 |
| abstract_inverted_index.algorithms | 47, 146 |
| abstract_inverted_index.associated | 72 |
| abstract_inverted_index.estimation | 63, 94 |
| abstract_inverted_index.expensive. | 67 |
| abstract_inverted_index.processing | 14 |
| abstract_inverted_index.properties | 27 |
| abstract_inverted_index.showcasing | 179 |
| abstract_inverted_index.Space-based | 0 |
| abstract_inverted_index.accelerated | 92 |
| abstract_inverted_index.algorithms, | 56 |
| abstract_inverted_index.algorithms. | 22 |
| abstract_inverted_index.assumptions | 71 |
| abstract_inverted_index.atmospheric | 139 |
| abstract_inverted_index.challenges, | 89 |
| abstract_inverted_index.conditions, | 140 |
| abstract_inverted_index.convergence | 125 |
| abstract_inverted_index.demonstrate | 135, 170 |
| abstract_inverted_index.large-scale | 185 |
| abstract_inverted_index.properties. | 52 |
| abstract_inverted_index.reflectance | 39, 54, 104 |
| abstract_inverted_index.retrievals. | 85 |
| abstract_inverted_index.uncertainty | 142 |
| abstract_inverted_index.Furthermore, | 68 |
| abstract_inverted_index.non-Gaussian | 159 |
| abstract_inverted_index.Probabilistic | 53 |
| abstract_inverted_index.distributions | 157 |
| abstract_inverted_index.hyperspectral | 84 |
| abstract_inverted_index.spectrometers | 2 |
| abstract_inverted_index.implementation | 119 |
| abstract_inverted_index.scientifically | 24, 50 |
| abstract_inverted_index.computationally | 66 |
| abstract_inverted_index.discipline-specific | 46 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 18 |
| citation_normalized_percentile.value | 0.82157809 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |