Identifying materials and retrieving their properties from spectral imagery is based on their spectral reflectance calculated from the ratio of reflected radiance to the incident irradiance. However, obtaining the true reflectances of materials within a vegetation canopy is challenging given the varying illumination conditions across the canopy – i.e., the irradiance incident on a surface inside the canopy – caused by its complex 3D structure. Instead, in remote sensing, reflectances are calculated from the ratio of the spectral radiance measured by the sensor to the top-of-canopy (TOC) spectral irradiance, resulting in apparent reflectances that can significantly differ from the true reflectance spectra. To address this issue, we present a physically based illumination correction method for retrieving the true reflectances from close-range hyperspectral TOC reflectance images. The method uses five spectral invariant parameters to predict the illumination conditions from TOC reflectance and compute the corrected spectrum using a physically based model. For computational efficiency, the spectrally invariant parameters were retrieved using random forest regression trained with Monte Carlo ray tracing simulations. The method was tested on close-range imaging spectroscopy data from dense and sparse vegetation canopies for which reference in situ spectral measurements were available. This work is a step toward resolving the 3D radiation regime in vegetation canopies from TOC hyperspectral imagery. The retrieved spectral invariants provide a physical connection to the structure of the observed vegetation canopy. The true spectra of artificial and natural materials in a vegetation canopy, determined under various illumination conditions, allow their more robust (bio)chemical characterization, opening new applications in vegetation monitoring and material detection, and machine learning makes it possible to apply the method rapidly to large hyperspectral image sets.

中文翻译：

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使用光谱不变量和随机森林回归对近距离高光谱图像进行照明校正


识别材料并从光谱图像中检索其特性是基于其光谱反射率，该光谱反射率是根据反射辐射度与入射辐照度的比率计算得出的。然而，鉴于其复杂的 3D 结构会导致整个树冠的照明条件不同（即入射到树冠内表面的辐照度），因此获得植被冠层内材料的真实反射率具有挑战性。相反，在遥感中，反射率是根据传感器测量的光谱辐射度与冠顶 （TOC） 光谱辐照度的比率来计算的，从而产生与真实反射光谱明显不同的表观反射率。为了解决这个问题，我们提出了一种基于物理的照明校正方法，用于从近距离高光谱 TOC 反射图像中检索真实反射率。该方法使用五个光谱不变参数来预测 TOC 反射率的照明条件，并使用基于物理的模型计算校正后的光谱。为了提高计算效率，使用蒙特卡洛光线追踪模拟训练的随机森林回归来检索光谱不变参数。该方法在来自密集和稀疏植被冠层的近距离成像光谱数据上进行了测试，这些植被冠层有参考原位光谱测量。这项工作是从 TOC 高光谱图像中解析植被冠层中 3D 辐射状态的一步。检索到的光谱不变量提供了与观察到的植被冠层结构的物理连接。 在各种照明条件下测定植被冠层中人造和天然材料的真实光谱，使其能够进行更稳健的（生）化学表征，在植被监测和材料检测方面开辟了新的应用，而机器学习使该方法能够快速应用于大型高光谱图像集。