Influence of the Viewing Geometry Within Hyperspectral Images Retrieved From Uav Snapshot Cameras

Abstract

ABSTRACT:Hyperspectral data has great potential for vegetation parameter retrieval. However, due to angular effects resulting from different sunsurface-sensor geometries, objects might appear differently depending on the position of an object within the field of view of a sensor. Recently, lightweight snapshot cameras have been introduced, which capture hyperspectral information in two spatial and one spectral dimension and can be mounted on unmanned aerial vehicles. This study investigates the influence of the different viewing geometries within an image on the apparent hyperspectral reflection retrieved by these sensors. Additionally, it is evaluated how hyperspectral vegetation indices like the NDVI are effected by the angular effects within a single image and if the viewing geometry influences the apparent heterogeneity with an area of interest. The study is carried out for a barley canopy at booting stage. The results show significant influences of the position of the area of interest within the image. The red region of the spectrum is more influenced by the position than the near infrared. The ability of the NDVI to compensate these effects was limited to the capturing positions close to nadir. The apparent heterogeneity of the area of interest is the highest close to a nadir.

Radiometric block adjustment of hyperspectral image blocks in the Brazilian environment

“…The results of Burkart et al (2015) showed differences in three vegetation indices by considering different geometries of view in a wheat study site. Lastly, Aasen (2016) published results about uncorrected BRDF effects and showed different spectral responses and different normalized difference vegetation index values for a barley field. These results highlight the importance of the correction of BRDF effects.…”

Assessment of Multi-Image Unmanned Aerial Vehicle Based High-Throughput Field Phenotyping of Canopy Temperature

“…But when an orthomosaic is generated from multiple overlapping images, each area of interest on the ground (e.g., a plot) is captured by multiple images with different viewing geometries and several options exist to extract the signature of this area (Aasen and Bolten, 2018). It has been shown that the different data processing and extraction approaches have an influence on the apparent reflectance in remote sensing data in the visible and nearinfrared, e.g., (Aasen, 2016;Aasen and Bolten, 2018). This results from the interaction of surface anisotropy (meaning that the signal is directionality dependent) and measurement geometry [expressed by the bi-directional reflectance distribution function; BRDF, (Nicodemus et al, 1977;Schaepman-Strub et al, 2006)], which also effects the apparent temperature (Jones et al, 2009;Cao et al, 2019).…”