Shadowy areas present a big hindrance in target detection from HSI, as the reflectance data received from target materials is significantly diminished when measured from shadowy areas. In this work, we perform an in-depth analysis of hyperspectral target detection on targets in full illumination and in partial or full shadows; and analyze how much target detection can be improved if the hyperspectral data is corrected at the regions of shadows. To do this, first, we detect the shadows using LiDAR, and propose a way to correct them in the hyperspectral image using the physical radiance model. Then, using three target detectors, namely the spectral angle mapper (SAM), adaptive coherence estimator (ACE) and matched filter (MF), we compare the results of target detection with and without shadow correction. We analyze our results based on the target material (red felt and blue felt targets), the background (grass or gravel), the amount of shadow (partial or full) and based on the time of the data collection (in the morning or at noon). Our results indicate several interesting observations: (i) the red-felt material is much harder to detect than the blue-felt material even though they are made up of the same material; but this gap in detection decreases significantly if shadow correction is performed using the radiance model, (ii) both the red-felt and the blue-felt targets are hard to detect earlier in the day when the rays from the sun are inclined; but there is not a significant difference in making the data collection in the morning or at noon if shadow correction is performed, and (iii) the shadow compensation dramatically increases the detection rates and boosts up the area under the receiver operating curve (AUC) from around 0,7-0,9 band to the 0,95-1,00 band.

In addition, we provide our shadow detection code¹, sky-view factor results and all the MODTRAN outputs for the parts of the Share2012 dataset used in this work. In doing so, we hope to provide a benchmark for researchers who would like to test their target detection or shadow correction algorithms on HSI-LiDAR data.

阴影区域是 HSI 目标检测的一大障碍，因为从阴影区域测量时从目标材料接收到的反射数据显着减少。在这项工作中，我们对全光照和部分或全阴影下目标的高光谱目标检测进行了深入分析；并分析如果在阴影区域校正高光谱数据可以提高多少目标检测。为此，首先，我们使用 LiDAR 检测阴影，并提出一种使用物理辐射模型在高光谱图像中校正它们的方法。然后，使用三个目标检测器，即光谱角度映射器（SAM）、自适应相干估计器（ACE）和匹配滤波器（MF），我们比较了有阴影校正和没有阴影校正的目标检测结果。我们根据目标材料（红色毛毡和蓝色毛毡目标）、背景（草或砾石）、阴影量（部分或全部）以及数据收集时间（早上或下午）来分析我们的结果。中午）。我们的结果表明了几个有趣的观察结果：(i) 红色毡材料比蓝色毡材料更难检测，即使它们由相同的材料组成；但是如果使用辐射模型进行阴影校正，这种检测差距会显着减少，（ii）当太阳光线倾斜时，红色毡和蓝色毡目标都很难在当天早些时候检测到；但是如果进行阴影校正，在早上或中午进行数据收集没有显着差异，

此外，我们提供了我们的阴影检测代码¹、天空视图因子结果以及本工作中使用的 Share2012 数据集部分的所有 MODTRAN 输出。在此过程中，我们希望为希望在 HSI-LiDAR 数据上测试目标检测或阴影校正算法的研究人员提供基准。