A workflow to minimize shadows in UAV-based orthomosaics

Abstract

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 a 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.

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