REFLECTANCE CALIBRATION SCHEME FOR AIRBORNE FRAME CAMERA IMAGES
The image quality of photogrammetric images is influenced by various effects from outside the camera. One effect is the scattered light from the atmosphere that lowers contrast in the images and creates a colour shift towards the blue. Another is the changing illumination during the day which resu...
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2012-07-01
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Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B7/1/2012/isprsarchives-XXXIX-B7-1-2012.pdf |
Summary: | The image quality of photogrammetric images is influenced by various effects from outside the camera. One effect is the scattered
light from the atmosphere that lowers contrast in the images and creates a colour shift towards the blue. Another is the changing
illumination during the day which results in changing image brightness within an image block. In addition, there is the so-called
bidirectional reflectance of the ground (BRDF effects) that is giving rise to a view and sun angle dependent brightness gradient in the
image itself. To correct for the first two effects an atmospheric correction with reflectance calibration is chosen. The effects have
been corrected successfully for ADS linescan sensor data by using a parametrization of the atmospheric quantities. Following
Kaufman et al. the actual atmospheric condition is estimated by the brightness of a dark pixel taken from the image. The BRDF
effects are corrected using a semi-empirical modelling of the brightness gradient. Both methods are now extended to frame cameras.
Linescan sensors have a viewing geometry that is only dependent from the cross track view zenith angle. The difference for frame
cameras now is to include the extra dimension of the view azimuth into the modelling. Since both the atmospheric correction and the
BRDF correction require a model inversion with the help of image data, a different image sampling strategy is necessary which
includes the azimuth angle dependence. For the atmospheric correction a sixth variable is added to the existing five variables
visibility, view zenith angle, sun zenith angle, ground altitude, and flight altitude – thus multiplying the number of modelling input
combinations for the offline-inversion. The parametrization has to reflect the view azimuth angle dependence. The BRDF model
already contains the view azimuth dependence and is combined with a new sampling strategy. |
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ISSN: | 1682-1750 2194-9034 |