An approach to the radiometric aerotriangulation of photogrammetric images
Harnessing the radiometric information provided by photogrammetric flights could be useful in increasing the thematic applications of aerial images. The aim of this paper is to improve relative and absolute homogenization in aerial images by applying atmospheric correction and treatment of bidirectional effects. We propose combining remote sensing methodologies based on radiative transfer models and photogrammetry models, taking into account the three-dimensional geometry of the images (external orientation and Digital Elevation Model). The photogrammetric flight was done with a Z/I Digital Mapping Camera (DMC) with a Ground Sample Distance (GSD) of 45cm. Spectral field data were acquired by defining radiometric control points in order to apply atmospheric correction models, obtaining calibration parameters from the camera and surface reflectance images. Kernel-driven models were applied to correct the anisotropy caused by the bidirectional reflectance distribution function (BRDF) of surfaces viewed under large observation angles with constant illumination, using the overlapping area between images and the establishment of radiometric tie points. Two case studies were used: 8-bit images with applied Lookup Tables (LUTs) resulting from the conventional photogrammetric workflow for BRDF studies and original 12-bit images (Low Resolution Color, LRC) for the correction of atmospheric and bidirectional effects. The proposed methodology shows promising results in the different phases of the process. The geometric kernel that shows the best performance is the Lidense kernel. The homogenization factor in 8-bit images ranged from 6% to 25% relative to the range of digital numbers (0255), and from 18% to 35% relative to levels of reflectance (0100) in the 12-bit images, representing a relative improvement of approximately 130%, depending on the band analyzed.
radiometric aerotriangulation aerial images atmospheric correction bidirectional effects calibration kernel-driven models