Geometric Calibration of Thermographic Cameras

  • Thomas Luhmann
  • Johannes Piechel
  • Thorsten Roelfs
Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 17)


This chapter presents an overview of thermal imaging sensors for photogrammetric close-range applications. In particular, it presents results of the geometric calibration of thermographic cameras as they are used for building inspection and material testing. Geometric calibration becomes evident for all precise geometric image operations, e.g. mosaicking of two or more images or photogrammetric 3D modelling with thermal imagery. Two different test fields have been designed providing point targets that are visible in the thermal spectral band of the cameras.

Five different cameras have been investigated. Four of them have solid state sensors with pixel sizes between 25 and 40 μm (i.e. size of single sensor element on the chip). One camera is working in scanning mode. The lenses for thermographic cameras are made of Germanium, which is, in contrast to glass, transparent to thermal radiation. Conventional imaging configurations (typically 20 images) have been used for camera calibration. Standard parameters for principal distance, principal point, radial distortion, decentring distortion, affinity and shear have been introduced into the self-calibrating bundle adjustment. All measured points are introduced as weighted control points. Image coordinates have been measured either in the professional software package AICON 3D Studio (ellipse operators), or in the software system Stereomess (least-squares template matching), developed by the Institute for Applied Photogrammetry and Geoinformatics of the Jade University of Applied Sciences Oldenburg.

The calibration results differ significantly from camera to camera. All lenses show relatively large decentring distortion and deviations from orthogonality of the image coordinate axes. Using a plane test field with heated lamps, the average image precision is 0.3 pixel while a 3D test field with circular reflecting targets results in imaging errors of 0.05 pixel.


Root Mean Square Camera Calibration Principal Point Bundle Adjustment Radial Distortion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Thomas Luhmann
    • 1
  • Johannes Piechel
    • 1
  • Thorsten Roelfs
    • 1
  1. 1.Institute for Applied Photogrammetry and Geoinformatics (IAPG)Jade University of Applied SciencesOldenburgGermany

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