A Workflow to Minimize Shadows in UAV-based Orthomosaics

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Journal of Unmanned Vehicle Systems, NRC Research Press
Shadows from buildings, terrain, and other elevated features represent lost and/or impaired data values that hinder the quality of optical images acquired under all but the most diffuse illumination conditions. This is particularly problematic in high-spatial-resolution imagery acquired from unmanned aerial vehicles (UAVs), which generally operate very close to the ground. However, the flexibility and low cost of re-deployment of the platform also presents opportunities, which we capitalize on in a new workflow designed to eliminate shadows from UAV-based orthomosaics. Our straightforward, three-step procedure relies on images acquired from two different UAV flights, where illumination conditions produce diverging shadow orientations: one before solar noon and another after. From this multi-temporal image stack, we first identify and then eliminate shadows from individual orthophoto components, then construct the final orthomosaic using a feature-matching strategy with the commercial software package Photoscan. The utility of our strategy is demonstrated over a treed-wetland study site in northwestern Alberta, Canada: a complex scene containing wide variety of shadows, which our workflow effectively eliminated. While shadow-reduced orthomosaics are generally less useful for feature-identification tasks that rely on the shadow element of image interpretation, they create a superior foundation for most other image-processing routines, including classification and change-detection.
UAV, Photogrammetry, Shadow Removal, Orthophoto, Remote Sensing
Rahman, M. M., McDermid, G. J., Mckeeman, T., & Lovitt, J. (2019). A Workflow to Minimize Shadows in UAV-based Orthomosaics. Journal of Unmanned Vehicle Systems, (ja). https://doi.org/10.1139/juvs-2018-0012