A simple but effective workflow to minimize shadow in UAV-based orthomosaics
In recent years, Unmanned Aerial Vehicles (UAVs) have become popular for various applications requiring aerial imaging - including environmental monitoring, emergency management, resource operations, inspections, filmography, and photography - due to their ease-of-use, relatively much lower operating cost, and the potential of very high spatial and temporal resolutions compared to traditional platforms. However, the presence of shadows from vegetation, terrain, and other elevated features represent lost and/or impaired data values that hinder the quality of optical images and a broad range of image-processing routines. Fortunately, the flexibility and low cost of re-deployment of the UAV platform also presents opportunities, which we capitalize on in a new workflow designed to eliminate shadows from UAV-based orthomosaics. Our three-step procedure (figure 1) 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.
Figure 1: A workflow for creating shadow-reduced orthomosaic from two-pass UAV photography.
We demonstrated our workflow in a study area located approximately 40 km north of Peace River in the Canadian province of Alberta, Canada: a complex treed bog containing wide variety of shadows. To evaluate the performance of our workflow, we compared a shadow-reduced orthomosaic to a traditional one obtained from a single flight. According to our analysis, 22.3% of the study area was covered by shadow in the traditional orthomosaic, for which it was not possible to map the surface/vegetation. On the other hand, the shadow-reduced orthomosaic was able to remove shadows from these areas and thereby reveal the underlying vegetation structure (figure 2).
Figure 2: A visual comparison of shadow-reduced (right) and raw (left) orthomosaic of scenes within the study area
To obtain the peer-reviewed version of our work, please follow the links below:
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