A physical-based atmospheric correction algorithm of unmanned aerial vehicles images and its utility analysis

Abstract

ABSTRACTUnmanned aerial vehicles (UAVs)-based environmental studies are gaining space in recent years due to their advantages of minimal cost, flexibility, and very high spatial resolution. Researchers can acquire imagery according to their schedule and convenience with the option of alternating the sensors working in visible, infrared, and microwave wavelengths. The recent developments in UAVs and in the associated image-processing techniques extend the fields of UAVs application. Inherent geometric deformation of UAVs images inevitably leads to burgeoning interest in exploring the geographical registration techniques of UAVs images preprocessing. However, atmospheric correction had been generally neglected due to the low altitudes of UAVs platforms. The path radiance of low-latitude atmosphere misleads the reflectance of target objects. Thus, a valid atmospheric correction is essential in the cases where vegetation indices (VIs) are adopted in vegetation monitoring. The off-the-shelf atmospheric correction algorithms adopted in satellite-based remote sensing are typically ill-suited for UAVs-based images due to the distinctly different altitudes and radiation transfer modes. This article identified the effect of atmospheric attenuation for spectral data collected by UAVs sensors of different altitudes and developed a physical-based atmospheric correction algorithm of UAVs images. Field-measured reflectance spectrum was essential in modelling. A sunny and dry day and a flat terrain were the two prerequisites to ensure the general application of the developed algorithm. A case study was subsequently carried out to verify the utility of the developed algorithm, and the results showed that VIs based on the UAVs images of different altitudes had a similar ability in vegetation assessment as ground-based recordings. However, the assessment accuracy could be clearly improved by using the developed atmospheric correction algorithm.