Intensity-Preserving Robust Fusion for Multi-Frame Spatial Heterodyne Spectral Recovery Toward Atmospheric Remote Sensing

Compact spatial heterodyne spectroscopy (SHS) is useful for atmospheric remote sensing, but repeated interferogram acquisition alone does not guarantee stable recovered spectral intensity. An intensity-preserving framework based on self-calibrated quality-gated robust fusion (SC-QGRF) was evaluated for multi-frame SHS spectral recovery. Frame weights are derived from image and spectral quality descriptors while avoiding framewise spectral normalization. Test spectral measurements were carried out using potassium and xenon lamps, which served as controlled laboratory cases for line spectrum and broadband spectrum recovery, respectively. Random Single, Image Average, Spectrum Mean, Spectrum Median, and the proposed strategy were compared under a common recovery and evaluation setting. Complete frame and exposure response analyses retained the main spectral profiles and showed no evident exposure-dependent intensity bias in this laboratory setting. Repeated subsampling provided the main stability evidence. SC-QGRF achieved the lowest variability in all four core metrics: coefficient of variation (CV) of K-AreaRatio = 0.0445%, CV of the full width at half maximum of the second K peak (K-FWHM2) = 0.0395%, CV of Xe-BandIntegral = 1.1770%, and standard deviation (STD) of Xe-SpectralCentroid = 0.0786 nm. The method can be used as a spectral quality control and fusion step for repeated SHS observations toward atmospheric remote sensing, while gas cell and field measurements remain necessary for retrieval-level validation.

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Publication Details

Journal
Atmosphere
Published
2026-07-16
DOI
https://doi.org/10.3390/atmos17070695
Primary Topic
Optical Polarization and Ellipsometry
Type
article
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article

Intensity-Preserving Robust Fusion for Multi-Frame Spatial Heterodyne Spectral Recovery Toward Atmospheric Remote Sensing

Wei Xiong, Zi Wang, Wei Luo, Xuanzhi Liao et al.
Atmosphere
Optical Polarization and Ellipsometry
article

Intensity-Preserving Robust Fusion for Multi-Frame Spatial Heterodyne Spectral Recovery Toward Atmospheric Remote Sensing

Wei Xiong, Zi Wang, Wei Luo, Xuanzhi Liao, Song Ye
article en

Abstract

Compact spatial heterodyne spectroscopy (SHS) is useful for atmospheric remote sensing, but repeated interferogram acquisition alone does not guarantee stable recovered spectral intensity. An intensity-preserving framework based on self-calibrated quality-gated robust fusion (SC-QGRF) was evaluated for multi-frame SHS spectral recovery. Frame weights are derived from image and spectral quality descriptors while avoiding framewise spectral normalization. Test spectral measurements were carried out using potassium and xenon lamps, which served as controlled laboratory cases for line spectrum and broadband spectrum recovery, respectively. Random Single, Image Average, Spectrum Mean, Spectrum Median, and the proposed strategy were compared under a common recovery and evaluation setting. Complete frame and exposure response analyses retained the main spectral profiles and showed no evident exposure-dependent intensity bias in this laboratory setting. Repeated subsampling provided the main stability evidence. SC-QGRF achieved the lowest variability in all four core metrics: coefficient of variation (CV) of K-AreaRatio = 0.0445%, CV of the full width at half maximum of the second K peak (K-FWHM2) = 0.0395%, CV of Xe-BandIntegral = 1.1770%, and standard deviation (STD) of Xe-SpectralCentroid = 0.0786 nm. The method can be used as a spectral quality control and fusion step for repeated SHS observations toward atmospheric remote sensing, while gas cell and field measurements remain necessary for retrieval-level validation.

AtmosphereVol. 17(7)
Chinese Academy of Sciences (CN), Hefei Institutes of Physical Science (CN), Beibu Gulf University (CN), Anhui Institute of Optics and Fine Mechanics (CN), Guilin University of Electronic Technology (CN)
Openalex Percentile: Top 15%
Optical Polarization and Ellipsometry
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