ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Highly Dense 3D Surface Generation Using Multi-image Matching

  • Received : 2011.01.15
  • Accepted : 2011.11.25
  • Published : 2012.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

This study presents an automatic matching method for generating a dense, accurate, and discontinuity-preserved digital surface model (DSM) using multiple images acquired by an aerial digital frame camera. The proposed method consists of two main procedures: area-based multi-image matching (AMIM) and stereo-pair epipolar line matching (SELM). AMIM evaluates the sum of the normalized cross correlation of corresponding image points from multiple images to determine the optimal height of an object point. A novel method is introduced for determining the search height range and incremental height, which are necessary for the vertical line locus used in the AMIM. This procedure also includes the means to select the best reference and target images for each strip so that multi-image matching can resolve the common problem over occlusion areas. The SELM extracts densely positioned distinct points along epipolar lines from the multiple images and generates a discontinuity-preserved DSM using geometric and radiometric constraints. The matched points derived by the AMIM are used as anchor points between overlapped images to find conjugate distinct points using epipolar geometry. The performance of the proposed method was evaluated for several different test areas, including urban areas.

Keywords

References

  1. C. Baillard and O. Dissard, "A Stereo Matching Algorithm for Urban Digital Elevation Models," Photogrammetric Eng. Remote Sens., vol. 66, no. 9, Sept. 2000, pp. 1119-1128.
  2. A. Alobeid, K. Jacobsen, and C. Heipke, "Comparison of Matching Algorithms for DSM Generation in Urban Areas from Ikonos Imagery," Photogrammetric Eng. Remote Sens., vol. 76, no. 9, Sept. 2010, pp. 1041-1050.
  3. J.S. Ku, K.M. Lee, and S.U. Lee, "Multi-mage Matching for a General Motion Stereo Camera Model," Proc. IEEE Int. Conf. Image Process., Chicago, IL, USA, 1998, pp. 608-612.
  4. H.G. Mass, "Automatic DEM Generation by Multi-image Image Feature Base Matching," Proc. ISPRS Congress Vienna, Vienna, Austria, 1996, pp. 484-489.
  5. R.T. Collins, "A Space-Sweep Approach to True Multi-image Matching," Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recognition, San Francisco, CA, USA, 1996, pp. 358-363.
  6. J. Shao, R. Mohr, and C. Fraser, "Multi-image Matching Using Segment Features," Proc. ISPRS Congress Amsterdam, Amsterdam, Netherlands, 2000, pp. 837-844.
  7. A.F. Elaksher, "Multi Image Matching of Straight Lines with Geometric Constraints," Proc. ISPRS GEOgraphic Object Based Image Analysis, Calgary, Alberta, Canada, 2008 (electronic form).
  8. F. Jung, V. Tollu, and N. Paparoditis, "Extracting 3D Edgel Hypotheses from Multiple Calibrated Images: A Step towards The Reconstruction of Curved and Straight Object Boundary Lines," Proc. ISPRS Photogrammetric Comput. Vision, Graz, Austria, 2002, pp. 100-104.
  9. F. Jung and N. Paparoditis, "Extracting 3D Free-Form Surface Boundaries of Man-Made Objects from Multiple Calibrated Images: A Robust, Accurate and High Resolving Power Edgel Matching and Chaining Approach," Proc. ISPRS Photogrammetric Image Analysis, Munich, Germany, 2003, pp. 39-44.
  10. L. Zhang, Automatic Digital Surface Model(DEM) Generation from Linear Array Images, doctoral dissertation, Swiss Federal Institute of Technology, Zurich, 2005.
  11. M.N. Pateraki, Adaptive Multi-image Matching for DEM Generation from Airborne Linear Array CCD Data, PhD dissertation, Swiss Federal Institute of Technology, Zurich, 2005.
  12. U.M. Leloglu, M. Roux, and H. Maitre, "Urban DEM with Three or More High-Resolution Aerial Images," Proc. ISPRS GIS between Visions Appl., Stuttgart, Germany, 1998, pp. 347-352.
  13. H. Hirschmuller, "Stereo Processing by Semiglobal Matching and Mutual Information," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 2, Feb. 2008, pp. 328-341.
  14. G. Zhang et al., "Consistent Depth Maps Recovery from a Video Sequence," IEEE Trans. Pattern Anal. Mach., vol. 31, no. 6, June 2009, pp. 974-988.
  15. D. Bulatov and J.E. Lavery, "Reconstruction and Texturing of 3D Urban Terrain from Uncalibrated Monocular Images Using L1 Splines," Photogrammetric Eng. Remote Sens., vol. 76, no. 4, Apr. 2010, pp. 439-449.
  16. T. Schenk, Digital Photogrammetry, vol. 1, Ohio: TerraScience, 1999.
  17. N. Paparoditis, C. Thom, and H. Jibrini, "Surface Reconstruction in Urban Areas from Multiple Views with Aerial Digital Frame Cameras," Proc. ISPRS Congress Amsterdam, Amsterdam, Netherlands, 2000 (CD ROM).
  18. Y. Furukawa and J. Ponce, "Accurate, Dense, and Robust Multiview Stereopsis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 8, Aug. 2010, pp. 1362-1376.
  19. T. Kanade, "A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment," IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no. 9, Sept. 1994, pp. 920-932.

Cited by

  1. Avaliação de uma técnica para geração de modelos digitais de superfície utilizando múltiplas imagens vol.20, pp.2, 2012, https://doi.org/10.1590/s1982-21702014000200016
  2. Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions vol.52, pp.2, 2015, https://doi.org/10.1080/15481603.2015.1008621
  3. Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence vol.16, pp.3, 2016, https://doi.org/10.3390/s16030412