Imaging Science MS Defense: Luke DeCoffe
MS Thesis Defense
Progressing sUAS-based Remote Sensing Data Collection Automation: Performance Analysis of the At-Altitude Radiance Ratio Method for Reflectance Conversion of Multispectral and Hyperspectral Remote Sensing Data
Luke DeCoffe
Imaging Science MS Candidate
Chester F. Carlson Center for Imaging Science, RIT
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Abstract:
In remote sensing, the conversion of at-sensor radiance to surface reflectance for each pixel in a scene, canonically known as reflectance conversion, is an essential component of many diagnostic analysis tasks. This process is crucial to the production of accurate information for a variety of applications, notably precision agriculture. 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 researched method, is attractive as it allows reflectance conversion to be carried out in real-time throughout data collection. Unlike ELM, AARR does not require calibrated samples of pre-measured reflectance to be placed in scene, and can account for changes in illumination conditions, which can substantially reduce the level of effort required for collection setup and subsequent data analysis. Illumination changes during a collection greatly impact the recorded scene radiance, which can confuse subsequent analysis results. For this research, an onboard, downwelling irradiance spectrometer integrated onto a small unmanned aircraft system (sUAS) is utilized to characterize the performance of AARR-generated reflectance from multispectral and hyperspectral radiance data at varying aircraft altitudes and is cross compared to the ELM approach. The observed error introduced by AARR is often acceptable depending on the application requirements and natural variation in the reflectance of the targets of interest. Furthermore, this work introduces several avenues to improve the AARR method based on additional experimentation.
Intended Audience:
Undergraduates, graduates, and experts. Those with interest in the topic.
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