Joint ISRF and Spectral Shift Estimation for Spectrometer Calibration Using Optimal Transport

The estimation of high-resolution spectrometer instrument spectral response functions (ISRFs) is crucial, for example in the context of remote sensing in order not to compromise the determination of trace gas concentrations. This paper introduces a new statistical model and an optimization algorithm for the joint estimation of ISRFs and spectral shifts. As a key novel ingredient, we investigate the use of optimal transport theory and the associated Wasserstein distance to estimate the spectral shifts, comparing this approach to the conventional <tex>$\ell_{2}$</tex> norm. As a second key ingredient, a sparse representation of the ISRFs is used to decompose these functions into a fixed dictionary of atoms. Results suggest that the proposed method performs well for small spectral shifts with both distances, while the Wasserstein distance proves particularly effective for estimating larger spectral shifts.

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Publication Details

Published
2025-09-08
DOI
https://doi.org/10.23919/eusipco63237.2025.11226436
Primary Topic
Remote-Sensing Image Classification
Type
article
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article

Joint ISRF and Spectral Shift Estimation for Spectrometer Calibration Using Optimal Transport

Herwig Wendt, Filip Elvander, Jean‐Yves Tourneret, Jean‐Michel Gaucel et al.
Remote-Sensing Image Classification
article

Joint ISRF and Spectral Shift Estimation for Spectrometer Calibration Using Optimal Transport

Herwig Wendt, Filip Elvander, Jean‐Yves Tourneret, Jean‐Michel Gaucel, Jihanne El Haouari, Christelle Pittet
article en

Abstract

The estimation of high-resolution spectrometer instrument spectral response functions (ISRFs) is crucial, for example in the context of remote sensing in order not to compromise the determination of trace gas concentrations. This paper introduces a new statistical model and an optimization algorithm for the joint estimation of ISRFs and spectral shifts. As a key novel ingredient, we investigate the use of optimal transport theory and the associated Wasserstein distance to estimate the spectral shifts, comparing this approach to the conventional <tex>$\ell_{2}$</tex> norm. As a second key ingredient, a sparse representation of the ISRFs is used to decompose these functions into a fixed dictionary of atoms. Results suggest that the proposed method performs well for small spectral shifts with both distances, while the Wasserstein distance proves particularly effective for estimating larger spectral shifts.

Centre National de la Recherche Scientifique (FR), Université Toulouse III - Paul Sabatier (FR), Université Fédérale de Toulouse Midi-Pyrénées (FR), Institut National Polytechnique de Toulouse (FR), Centre National d'Études Spatiales (FR), Université Toulouse-I-Capitole (FR), Institut de Recherche en Informatique de Toulouse (FR), Thales (France) (FR), Centre Hospitalier de Cannes (FR), Université Toulouse - Jean Jaurès (FR), Institut Polytechnique de Bordeaux (FR), École Nationale Supérieure d'Électrotechnique, d'Électronique, d'Informatique, d'Hydraulique et des Télécommunications (FR), Aalto University (FI)
Openalex Percentile: Top 54%
Remote-Sensing Image Classification
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Joint ISRF and Spectral Shift Estimation for Spectrometer Calibration Using Optimal Transport — Herwig Wendt, Filip Elvander, et al. · (2025) | TGRS Research Map | TGRS