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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.05298 |
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| _version_ | 1866916197597970432 |
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| author | Haouari, Jihanne El Gaucel, Jean-Michel Pittet, Christelle Tourneret, Jean-Yves Wendt, Herwig |
| author_facet | Haouari, Jihanne El Gaucel, Jean-Michel Pittet, Christelle Tourneret, Jean-Yves Wendt, Herwig |
| contents | Accurate estimates of Instrument Spectral Response Functions (ISRFs) are crucial in order to have a good characterization of high resolution spectrometers. Spectrometers are composed of different optical elements that can induce errors in the measurements and therefore need to be modeled as accurately as possible. Parametric models are currently used to estimate these response functions. However, these models cannot always take into account the diversity of ISRF shapes that are encountered in practical applications. This paper studies a new ISRF estimation method based on a sparse representation of atoms belonging to a dictionary. This method is applied to different high-resolution spectrometers in order to assess its reproducibility for multiple remote sensing missions. The proposed method is shown to be very competitive when compared to the more commonly used parametric models, and yields normalized ISRF estimation errors less than 1%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05298 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | In-Flight Estimation of Instrument Spectral Response Functions Using Sparse Representations Haouari, Jihanne El Gaucel, Jean-Michel Pittet, Christelle Tourneret, Jean-Yves Wendt, Herwig Numerical Analysis Machine Learning Accurate estimates of Instrument Spectral Response Functions (ISRFs) are crucial in order to have a good characterization of high resolution spectrometers. Spectrometers are composed of different optical elements that can induce errors in the measurements and therefore need to be modeled as accurately as possible. Parametric models are currently used to estimate these response functions. However, these models cannot always take into account the diversity of ISRF shapes that are encountered in practical applications. This paper studies a new ISRF estimation method based on a sparse representation of atoms belonging to a dictionary. This method is applied to different high-resolution spectrometers in order to assess its reproducibility for multiple remote sensing missions. The proposed method is shown to be very competitive when compared to the more commonly used parametric models, and yields normalized ISRF estimation errors less than 1%. |
| title | In-Flight Estimation of Instrument Spectral Response Functions Using Sparse Representations |
| topic | Numerical Analysis Machine Learning |
| url | https://arxiv.org/abs/2404.05298 |