Spectral Reconstruction Method for Specific Spatial Heterodyne Interferograms Based on Deep Neural Networks Article Swipe
Wei Luo
,
Song Ye
,
Ziyang Zhang
,
Wei Xiong
,
Dacheng Li
,
Jun Wu
,
Xinqiang Wang
,
Shu Li
,
Fangyuan Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/atmos16080909
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/atmos16080909
The spatial heterodyne spectrometer is an interferometric spectrometer specifically designed for particular detection targets, capable of achieving ultra-high spectral resolution within a designated spectral range. As the demand for signal detection accuracy continues to increase, the extraction of accurate target spectra from spatial heterodyne interferograms has become increasingly important. This paper applies a deep neural network to the spectral reconstruction of specific spatial heterodyne interferograms. The spectral reconstruction model, SRDNN, was trained using CO2 data simulated by the SCIATRAN radiative transfer model and the principles of spatial heterodyne spectroscopy. The results indicate that SRDNN has excellent CO2 spectral reconstruction performance, with an evaluation index R2 of 0.9943 and an MSE of 0.00021. The average difference between the reconstructed spectra and the target spectra is only 0.371%. Furthermore, the method was further validated using experimental data obtained from a spatial heterodyne spectrometer. The remarkable spectral reconstruction results and excellent evaluation indicators once again demonstrated the universality and effectiveness of the method. Finally, the robustness of the method was studied using noisy experimental data. The results demonstrate that the method can accurately reconstruct spectra from interferograms with slight noise without requiring additional processing, simplifying the spectral reconstruction process. This work is expected to provide novel methods and effective solutions for the spectral reconstruction of specific targets detected by spatial heterodyne spectrometers.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/atmos16080909
- OA Status
- gold
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413095833
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- OpenAlex ID
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https://openalex.org/W4413095833Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/atmos16080909Digital Object Identifier
- Title
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Spectral Reconstruction Method for Specific Spatial Heterodyne Interferograms Based on Deep Neural NetworksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-28Full publication date if available
- Authors
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Wei Luo, Song Ye, Ziyang Zhang, Wei Xiong, Dacheng Li, Jun Wu, Xinqiang Wang, Shu Li, Fangyuan WangList of authors in order
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https://doi.org/10.3390/atmos16080909Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/atmos16080909Direct OA link when available
- Concepts
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Spectrometer, Remote sensing, Optics, Computer science, Imaging spectrometer, Robustness (evolution), Image resolution, Spectral resolution, Spectral line, Heterodyne (poetry), Interferometry, Physics, Geology, Acoustics, Chemistry, Astronomy, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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