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%.

翻译：高分辨率光谱仪的精确表征离不开对仪器光谱响应函数（ISRF）的准确估计。光谱仪由多种光学元件构成，这些元件可能引入测量误差，因此需要尽可能精确地建模。当前通常采用参数化模型来估计这些响应函数，但此类模型难以全面考虑实际应用中ISRF形态的多样性。本文研究了一种基于字典原子稀疏表示的新型ISRF估计方法。为评估该方法在多次遥感任务中的可重复性，将其应用于不同高分辨率光谱仪。结果表明，与更常用的参数化模型相比，所提方法具有显著竞争力，且归一化ISRF估计误差低于1%。