Research for verification and calibration
of Multi-spectral aerial photographing system(PKNU 3)
2. PKNU 3 sensor
Multi-spectral aerial photographing system designated the PKNU 3 consists of a multi-spectral
camera (REDLAKE MS 4000) that can take R, G, B, IR images simultaneously and also a
thermal infrared camera (Raytheon IRPro).
2.1. REDLAKE MS 4000
The REDLAKE MS 4000 sensor is a triple CCD camera that can take R,G,B,IR images
simultaneously so it can produce RGB and CIR 1600 ×1200 pixels(7.4 . per pixel) images of
the target area.
Figure 1 B,G,R,IR Band and FWHM of MS 4000 sensor
The green and blue bands are detected on one CCD with a Bayer pattern consisting of rows of
red-green-red-green and blue-green-blue-green pixels. A monochrome CCD sensor acquires the
red plane at full resolution.
Images captured with the MS 4000 RGB/CIR triple CCD camera such as the one used in this
study has a comparably higher resolution than images acquired with a single-chip color camera
because in the RGB/CIR configuration, the red image is acquired at full resolution.
2.2 Raytheon IRPro
The thermal infrared camera, a Raytheon IRPro used in this study senses the energy of 7~14
. wavelength as still and moving pictures and displays through an LCD viewer. Thermal
energy patterns on images are displayed via 5 colors (red, orange, yellow, green, and blue) and
temperatures are expressed in terms of brightness values.
3. Calibration and correction of Sensors
3.1. Multi-spectral camera (RGB/CIR MS 4000 sensor)
Prior to obtaining data from aerial photography, the RGB/CIR MS 4000 sensor was first
calibrated so that geometric and radiometric accuracy of images taken by PKNU 3 would be
possible.
3.1.1 Geometric correction of multi-spectral camera
Camera lens distortion due to the geometric structure of lens is due to varying degrees of
radial and tangential distortion. Tangential distortion has typically a much smaller effect than
radial distortion and can thus be safely disregarded during calibration. Thus, we focus on the
correction of radial distortion in this study.
A panel of 121 evenly arrayed GCPs was utilized for determining radial lens distortion using
Total station. Then, that panel was taken up as an image by the MS 4000 sensor. Ground
coordinates(x, y, z) were calculated using the horizontal angle (HA), vertical angle (VA), and
the slope distance (SA) values surveyed by Total station as well as the computed radial distance
of the ground coordinates, the radial distance of the image(r), and the radial distance r).
Lens distortions were then computed by comparing between ground coordinates (set as a
reference) surveyed by Total station and the converted coordinates from pixel numbers on the
image of the panel in this paper. The lens distortion characteristic was ascertained by setting the
coefficients of radial distortion as K 0 =6.4817200e-3, K 1 =-4.4270500e-4, K 2 =3.9596100e-06
using eq. (3-1).
r: radial distortion, r: radial distance, k 0 ~k n : coefficients of radial distortion, í : variation)
Image coordinates(x, y) corrected for the radial lens distortions through eq. (3-2) were
calculated. Precision of x, y and RMSE (Root Mean Square Error) on the image before and after
correction of the lens distortion are shown in table 1.
(3-2)
Table 1 Precision
Consider that the reason for the small lens distortion as the 0.57~0.92 range pixel distortion
determined above is due to the utilization of the central 12% of the available photographic area
of the lens; where lens distortion is typically lowest. Thus, it is possible to obtain geometrically
precise images of less than 1 pixel in lens distortion through calibration and lens distortion
compensation procedures of the REDLAKE MS 4000 sensor.
