Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.

中文翻译：

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通过基于学习的方法对 PRISMA 数据进行大气补偿

大气补偿 (AC) 允许从测量的传感器辐射率中检索反射率，并且是定量开发高光谱数据的一项基本且关键的任务。最近，已经提出了一种名为 LBAC 的基于学习 (LB) 的方法，用于可见光和近红外 (VNIR) 光谱范围内机载高光谱数据的 AC。LBAC 使用参数回归函数，其参数通过基于合成数据的策略学习，该策略解释 (1) 基于物理的辐射传输模型，(2) 表面反射光谱的可变性，以及 (3)随机噪声和光谱校准误差的影响。在这项工作中，我们从两个不同的方面扩展了 LBAC：（1）数据采集平台和（2）传感器覆盖的光谱范围。特别，我们建议将 LBAC 扩展到在电磁波谱的 VNIR 和短波红外 (SWIR) 部分运行的星载高光谱传感器。我们特别提到了 PRISMA（PRecursore IperSpettrale della Missione Applicativa）任务的传感器，以及意大利航天局最近的地球观测任务，这为提高星载高光谱数据的科学和商业应用知识提供了一个很好的机会。此外，我们引入了一种基于曲线拟合的程序，用于直接利用 LBAC 提供的反射数据来估算大气中的柱状水蒸气含量。介绍和讨论了在四个不同的 PRISMA 高光谱图像上获得的结果。