Advertisement

Geometric Calibration of Thermographic Cameras

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

Abstract

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.

Keywords

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.

References

  1. Buyuksalih G, Petrie G (1999) Geometric and radiometric calibration of frame-type infrared imagers. ISPRS joint workshop sensors and mapping from space 1999, HannoverGoogle Scholar
  2. Dereniak EL, Boreman GD (1996) Infrared detectors and systems. Wiley-Interscience, New York, 561ppGoogle Scholar
  3. Ehlers M, Klonusa S, Åstrand PJ, Rosso P (2010) Multi-sensor image fusion for pansharpening in remote sensing. Int J Image Data Fusion 1(1):25–45CrossRefGoogle Scholar
  4. Fouad NA, Richter T (2008) Leitfaden Thermografie im Bauwesen. Fraunhofer IRB Verlag, Stuttgart, 127ppGoogle Scholar
  5. Fraser CS (1997) Digital camera self-calibration. ISPRS J Photogramm Remote Sens 52:149–159CrossRefGoogle Scholar
  6. Godding R (1993) Ein photogrammetrisches Verfahren zur Überprüfung und Kalibrierung digitaler Bildaufnahmesysteme. Z Photogramm Fernerkund 2:82–90Google Scholar
  7. Hierl T (2008) Hochauflösende Infrarot-Detektormatrizen. In: Bauer N (ed) Handbuch zur Industriellen Bildverarbeitung. Fraunhofer IRB Verlag, Stuttgart, pp 41–46Google Scholar
  8. Kaplan H (2007) Practical applications of infrared thermal sensing and image equipment. SPIE Publications, Bellingham, 192ppCrossRefGoogle Scholar
  9. Le Noc L, Tremblay B, Martel A, Chevalier C, Blanchard N, Morissette M, Mercier L, Duchesne F, Gagnon L, Couture P, Lévesque F, Desnoyers N, Demers M, Lamontage F, Jerominek H, Bergeron A (2010) 1280 × 960 pixel microscanned infrared imaging module. In: Infrared technology and applications XXXVI. Proceedings of SPIE, vol 7660: 766021-766021-10, Orlando, 2010Google Scholar
  10. Luhmann T (2010) Erweiterte Verfahren zur geometrischen Kamerakalibrierung in der Nahbereichsphotogrammetrie, Habilitationsschrift, Deutsche Geodätische Kommission, Reihe C, Nr. 645. Verlag der Bayerischen Akademie der Wissenschaften in Kommission beim Verlag C. H. Beck, MünchenGoogle Scholar
  11. Luhmann T, Robson S, Kyle S, Harley I (2006) Close range photogrammetry. Whittles Publishing, Dunbeath, 500ppGoogle Scholar
  12. Luhmann T, Ohm J, Piechel J, Roelfs T (2010) Geometric calibration of thermographic cameras. In: International archives of photogrammetry, remote sensing and spatial information sciences, vol XXXVIII, Part 5 Commission V symposium, Newcastle upon Tyne, 2010, pp 411–416Google Scholar
  13. Nolting J (2007) Detektoren für optische Strahlung. DOZ Optometrie 4-2007:50–56Google Scholar
  14. Planck M (1900) Zur Theorie des Gesetzes der Energieverteilung im Normalspectrum. Verhandlungen der Deutschen physikalischen Gesellschaft 2(17):237–245, BerlinGoogle Scholar
  15. Schuster N, Kolobrodov VG (2004) Infrarotthermographie. Wiley-VCH Verlag, Weinheim, 354ppCrossRefGoogle Scholar
  16. Toet A, van Ruyven JJ, Valeton JM (1989) Merging thermal and visual images by a contrast pyramid. Opt Eng 28(7):789–792CrossRefGoogle Scholar
  17. Wolfe WL, Zissis GJ (1985) The infrared handbook. Environmental Research Institute of Michigan, Ann Arbor, 1700ppGoogle Scholar

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

Personalised recommendations