Assessment of panchromatic sharpening algorithms typically starts with a high resolution Hyperspectral Image (HSI), which is then spatially degraded so that after the sharpening process, the result can be compared to the original and analyzed for accuracy. This leads to questions about quantitative assessments based solely on simulated low resolution data. To address this problem, a multi-resolution hyperspectral data set was collected by researchers from the Rochester Institute of Technology (RIT) in Henrietta, NY on July 24th 2020. Imagery of 48 felt targets, ranging in size from 5 cm to 30 cm and in six different colors, was collected using RIT's MX-1 UAS imaging system, which is designed to collect 272 spectral bands from approximately 400nm to 1000nm. Three flights were performed and images of the target scene were collected at flight altitudes of 30m, 60m, and 120m. The resulting imagery possesses ratios of 2:1 and 4:1 spatial resolution relative to the lowest altitude flight. The goal of this imaging campaign was to create a data set that will be used to test hyperspectral pansharpening algorithms currently under development at RIT. The radiometric accuracy of sharpening algorithms can be better ascertained through quantitative analysis of their results after being applied to this non-simulated multiresolution hyperspectral data set. This presentation will summarize the process of planning, creating, collecting and packaging the dataset which will be provided to RIT researchers and will also available for download online through RIT.
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