Digital Imaging and Remote Sensing Laboratory, Chester F. Carlson Center for Imaging Science, College of Science, Rochester Institute of Technology, 54 Lomb Memorial Drive, Rochester, NY 14623, USA.
Department of Electrical and Computer Engineering Technology, College of Engineering Technology, Rochester Institute of Technology, 15 Lomb Memorial Drive, Rochester, NY 14623, USA.
Sensors (Basel). 2022 Dec 28;23(1):320. doi: 10.3390/s23010320.
In remote sensing, the conversion of at-sensor radiance to surface reflectance for each pixel in a scene is an essential component of many analysis tasks. The empirical line method (ELM) is the most used technique among remote sensing practitioners due to its reliability and production of accurate reflectance measurements. However, the at-altitude radiance ratio (AARR), a more recently proposed methodology, is attractive as it allows reflectance conversion to be carried out in real time throughout data collection, does not require calibrated samples of pre-measured reflectance to be placed in scene, and can account for changes in illumination conditions. The benefits of AARR can substantially reduce the level of effort required for collection setup and subsequent data analysis, and provide a means for large-scale automation of remote sensing data collection, even in atypical flight conditions. In this study, an onboard, downwelling irradiance spectrometer integrated onto a small unmanned aircraft system (sUAS) is utilized to characterize the performance of AARR-generated reflectance from hyperspectral radiance data under a variety of challenging illumination conditions. The observed error introduced by AARR is often on par with ELM and acceptable depending on the application requirements and natural variation in the reflectance of the targets of interest. Additionally, a number of radiometric and atmospheric corrections are proposed that could increase the accuracy of the method in future trials, warranting further research.
在遥感中,将传感器辐亮度转换为场景中每个像素的地表反射率是许多分析任务的重要组成部分。经验线性法(ELM)是遥感从业者最常用的技术,因为它可靠且能生成准确的反射率测量值。然而,最近提出的方法——高空辐亮度比(AARR)很有吸引力,因为它允许在整个数据采集过程中实时进行反射率转换,不需要在场景中放置预先测量反射率的校准样本,并且可以考虑光照条件的变化。AARR 的好处可以大大减少采集设置和后续数据分析所需的工作量,并为遥感数据采集的大规模自动化提供一种手段,即使在非典型的飞行条件下也是如此。在本研究中,将小型无人机系统(sUAS)上集成的机载下向辐照度光谱仪用于在各种具有挑战性的光照条件下从高光谱辐亮度数据中表征 AARR 生成的反射率的性能。由 AARR 引入的观测误差通常与 ELM 相当,并且可以接受,具体取决于应用要求和感兴趣目标反射率的自然变化。此外,还提出了一些辐射和大气校正方法,这些方法可以在未来的试验中提高方法的准确性,值得进一步研究。