1471 Quantifying Atmospheric Effects on Unmanned Aerial System Imagery

Wednesday, 25 January 2017
4E (Washington State Convention Center )
Christopher M. Zarzar, Mississippi State University, Mississippi State, MS; and P. Dash, J. L. Dyer, and R. J. Moorhead II

Unmanned aerial systems (UAS), and especially small UAS (sUAS), provide flexible temporal, spectral, and spatial resolutions while maintaining a relative ease of implementation compared to commonly used data collection tools (e.g. satellite, ground survey, and manned aircraft). Furthermore, UAS can be flown at relatively low altitudes allowing the systems to sample below the cloud deck, which has obvious advantages for data collection during inclement weather conditions. The increasing use of UAS in research begs the question of whether common satellite and aircraft remote sensing principles must also be followed when post-processing UAS imagery. There are two common image post-processing practices applied when time series data are used: 1) the normalization for illumination and 2) the removal of atmospheric effects. The first of these practices should always be followed when working with time series UAS imagery; however, in question is whether the second practice is also required for UAS imagery. While the effects of molecular (Rayleigh) and aerosol (Mie) scattering of light are apparent in satellite and manned imagery, the question remains if atmospheric effects have an impact on imagery collected by UAS flown at lower altitudes. To address this question, this project utilizes imagery collected by a sUAS during an experiment conducted at North Farm located on Mississippi State University's campus. Imagery was collected at numerous flight altitudes during a mostly clear sky day from the hours of 11:00 to 13:00 CDT. Imagery from each flight altitude is compared to ground collected imagery to determine if there is a critical flight altitude when the images are significantly different. This information is vital for image post-processing efforts of subsequent research using time series UAS imagery as it provides a baseline for when the removal of atmospheric effects is required.
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