In-Flight Estimation of Instrument Spectral Response Functions Using Sparse Representations
This work addresses the need for better ISRF estimation in remote sensing missions, offering a competitive alternative to current methods, though it appears incremental as it builds on sparse representation techniques.
The paper tackled the problem of accurately estimating Instrument Spectral Response Functions (ISRFs) for high-resolution spectrometers, which is crucial for measurement characterization, by proposing a sparse representation method that achieves normalized ISRF estimation errors less than 1% and outperforms existing parametric models.
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%.