Mitigation of variable illumination effects in hyperspectral imagery

“该项目将进行可行性研究，旨在提高航空高光谱遥感的运作效率（即降低成本、增加包络面、改善利用）。”关键目标将是发展和评价减轻可变光照，特别是云阴影对恢复表面反射率的影响的技术。这些技术将在大量的经验和建模数据上进行开发和测试。主要关注的领域是；*通过辐射建模和经验数据收集，确定at传感器的辐射光谱是否揭示了有关当地照明条件的信息。这旨在识别嵌入在at传感器辐射中的特征，这些特征可能提供有关当地照明条件的信息。现有的技术包括经验推导的“阴影指数”，使用几个选定的波段；创新之处在于检查整个光谱以寻求改进的技术。*利用光照场（如上所述）的统计数据，包括在阴影中，设计一种缓解方法。这种方法是先前发表的“不变性”技术的扩展，旨在对变化的大气和环境因素具有鲁棒性。这些决定了包含由这些因素引起的大部分方差的子空间。然后，这些不需要的尺寸将从at传感器特征中“投影”出来。这些技术被“现成的”辐射传输模型限制在晴朗的天空条件下。创新之处在于，在确定不变子空间时考虑了云的衰减效应，从而实现了在更大范围的环境条件下的稳健开发。*利用时间序列、无云航空影像及卫星影像，发展经验校正技术。这种做法的动机是卫星图像的日益频繁和可用性。已发表的技术粗略地将阴影像素替换为大约同一时间获取的图像中相应的无云像素。这种方法不适用于具有不同光谱和空间响应的源（即卫星和机载）。创新之处在于开发一种技术，通过卫星（例如Sentinel-2）多光谱图像对高光谱图像中的云阴影进行校正。”

"This project will conduct a feasibility study aimed at improving the operational effectiveness (i.e. reduce costs, increase envelope, improve exploitation) of airborne hyperspectral remote sensing. The key objective will be to develop and evaluate techniques to mitigate the effects of variable illumination, in particular cloud shadow, on the recovery of surface reflectance. The techniques will be developed and tested on a significant volume of empirical and modelled data. The main areas of focus are;* To determine if the at-sensor radiance spectra reveal information regarding the local illumination conditions via radiative modelling and empirical data collection. This seeks to identify traits embedded in the at-sensor radiance that may provide information regarding the local illumination conditions. Existing techniques include empirically derived 'shadow indices' that use a few selected bands; the innovation is to examine the entire spectrum to seek an improved technique.* To exploit the statistics of the illumination field (determined above), including in shadow, to devise a mitigation approach. This approach is an extension of previously published 'invariance' techniques designed to be robust to varying atmospheric and environmental factors. These determine the subspace which contains the majority of variance caused by these factors. These unwanted dimensions are then 'projected out' of the at-sensor signature. These techniques have been limited to clear sky conditions by 'off-the-shelf' radiative transfer models. The innovation is to include the attenuating effects of cloud in the determination of an invariant subspace thus enabling robust exploitation over a wider range of environmental conditions.* To develop a empirical correction technique exploiting time series, cloud-free airborne and satellite imagery. This approach is motivated by the increasing regularity and availability of satellite imagery. Published techniques crudely replace shadowed pixels with the corresponding cloud-free pixel from an image acquired at around the same time. This approach is not suitable to be applied to sources with differing spectral and spatial response (i.e. satellite and airborne). The innovation is to develop a technique in which a correction for cloud shadow in the hyperspectral image is derived from that of a satellite (e.g. Sentinel-2) multi-spectral image."