| Autor: |
DeCoffe LJR; 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., Conran DN; 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., Bauch TD; 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., Ross MG; 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., Kaputa DS; Department of Electrical and Computer Engineering Technology, College of Engineering Technology, Rochester Institute of Technology, 15 Lomb Memorial Drive, Rochester, NY 14623, USA., Salvaggio C; 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. |
| Abstrakt: |
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. |
