A convergent image configuration for DEM extraction that minimises the systematic effects caused by an inaccurate lens model

The internal geometry of consumer‐grade digital cameras is generally considered unstable. Research conducted recently at Loughborough University indicated the potential of these sensors to maintain their internal geometry. It also identified residual systematic error surfaces or “domes”, discernible in digital elevation models (DEMs), caused by slightly inaccurate estimated lens distortion parameters. This paper investigates these systematic error surfaces and establishes a methodology to minimise them. Initially, simulated data was used to ascertain the effect of changing the interior orientation parameters on extracted DEMs, specifically the lens model. Results presented demonstrate the relationship between “domes” and inaccurately specified lens distortion parameters. The stereopair remains important for data extraction in photogrammetry, often using automated DEM extraction software. The photogrammetric normal case is widely used, in which the camera base is parallel to the object plane and the optical axes of the cameras intersect the object plane orthogonally. During simulation, the error surfaces derived from extracted DEMs using the normal case were compared with error surfaces created using a mildly convergent geometry. In contrast to the normal case, the optical camera axes intersect the object plane at the same point. Results of the simulation process clearly demonstrate that a mildly convergent camera configuration eradicates the systematic error surfaces. This result was confirmed through practical tests and demonstrates that mildly convergent imagery effectively improves the accuracies of DEMs derived with this class of sensor.

[1]  John G. Fryer,et al.  On the accuracy of heighting from aerial photographs and maps: Implications to process modellers , 1994 .

[2]  Clive S. Fraser,et al.  Digital camera self-calibration , 1997 .

[3]  D. Gennery,et al.  Calibration and Orientation of Cameras in Computer Vision , 2001 .

[4]  Horst A. Beyer,et al.  System Calibration Through Self-Calibration , 2001 .

[5]  Jim H. Chandler,et al.  The accuracy of a river bed moulding/casting system and the effectiveness of a low‐cost digital camera for recording river bed fabric , 2003 .

[6]  R. Reulke,et al.  Remote Sensing and Spatial Information Sciences , 2005 .

[7]  Mj Grenness,et al.  Stereo‐Photogrammetric Mapping of Tooth Replicas Incorporating Texture , 2005 .

[8]  J. Fryer,et al.  Metric capabilities of low‐cost digital cameras for close range surface measurement , 2005 .

[9]  Jim H. Chandler,et al.  THE ARCHIVAL PHOTOGRAMMETRIC TECHNIQUE: FURTHER APPLICATION AND DEVELOPMENT , 2006 .

[10]  Juha Hyyppä,et al.  Geometric test field calibration of digital photogrammetric sensors , 2006 .

[11]  C. Fraser.  EVOLUTION OF NETWORK ORIENTATION PROCEDURES , 2006 .

[12]  S. I. Granshaw,et al.  BUNDLE ADJUSTMENT METHODS IN ENGINEERING PHOTOGRAMMETRY , 2006 .

[13]  Zhilin Li,et al.  ON THE MEASURE OF DIGITAL TERRAIN MODEL ACCURACY , 2006 .

[14]  C. Fraser,et al.  Digital camera calibration methods: Considerations and comparisons , 2006 .

[15]  John G. Fryer,et al.  The Development And Application Of A Simple Methodology For Recording Rock Art Using Consumer‐Grade Digital Cameras , 2007 .

[16]  Jim H. Chandler,et al.  Geometric consistency and stability of consumer‐grade digital cameras for accurate spatial measurement , 2007 .