CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: DENSE MATCHING IN HIGH RESOLUTION OBLIQUE AIRBORNE IMAGES M. Gerke

Document type:: Monograph

Structure type:: Chapter

i£3BB—Si—jMBäMMBB—MBK HBasoBBBBBGSKS r 9K CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation matches here), but the overall accuracy seems to be better, see e.g. the height of the buildings. Also the detailed look from ver tical shows less noise, compared to the two-fold matches before. 5 CONCLUSIONS AND OUTLOOK This paper reports about the utilization of the dense image mat ching technique Semi-Global-Matching to a set of high resolu tion oblique airborne images. The images were acquired from different platforms and thus in different configurations. The com parison of the depth maps from matching with a reference com puted from LIDAR data showed that roughly 70% of all mat ches are within an error range of ± 3pixel, however, also the residual errors from camera calibration and orientation have an impact on this evaluation. The remaining matches can be consid ered as blunders. How can those blunders be removed and the noise level be reduced? If multiple overlaps are available, sophis ticated error analysis prior to triangulation is feasible (Hirsch müller, 2008). Also the method as applied here shows good re sults, namely to eliminate wrong matches through linking mat ches of adjacent images and applying a double check through a direct match. Other authors use the much stronger trinocular stereo geometry for matching (Heinrichs et al., 2007), or apply a similarity criterion for multiple views directly (Besnerais et al., 2008). If only two-fold overlap is available - as mostly in fa cade observations from oblique images - one method could be to incorporate reliable SIFT features within the SGM approach di rectly: The disparities as defined by them can be used to reduce the respective matching cost. The point cloud as resulted from the triangulation of the respec tive matches revealed the sensitivity to the ray intersection angle and base length of cameras. For instance in the case of consec utive FLI-MAP images the theoretic standard deviation of trian gulated points in viewing (depth) direction is - due to the small effective baseline - around lm, but perpendicular to that - due to the low flying height - around 2cm only. In the shown ex amples these theoretic measures were confirmed. In the tested images from Pictometry the intersection geometry is better be cause of the longer baseline. In general, the overall structures on the building faces are well represented, but the noise reduction needs further attention. In the current research the focus is put on the automatic detec tion and extraction of buildings in oblique images. Here, the point cloud as derived from the matching can give valuable cues. Another issue concerns the derivation of a more complete cove rage by merging the point clouds as derived from different view ing directions. At least for the roof areas this can be done in a similar manner as shown above, namely through linking mat ches, since the majority of roof areas are visible from multiple directions. ACKNOWLEDGEMENTS I want to thank Daniel Oram for providing the code for general rectification on his homepage 6 . Further I like to thank Matthias Heinrichs, TU Berlin, for providing me with his code for Semi- Global-Matching. I also want to thank BLOM Aerofilms for pro viding the Pictometry dataset. Finally I would like to thank the anonymous reviewer for their comments. REFERENCES Albertz, J. and Kreiling, W., 1980. Photogrammetrisches Taschenbuch. 3. edn, Herbert Wichmann, Karlsruhe. Besnerais, G. L., Sanfourche, M. and Champagnat, F, 2008. Dense height map estimation from oblique aerial image se quences. Computer Vision and Image Understanding 109(2), pp. 204 - 225. Früh, C., Sammon, R. and Zakhor, A., 2004. Automated texture mapping of 3d city models with oblique aerial imagery. In: 3D Data Processing Visualization and Transmission, International Symposium on, pp. 396-403. Gerke, M., 2008. Dense image matching in airborne video se quences. In: ISPRS: XXI congress : Silk road for information from imagery, Vol. XXXVII-B3b, International Archives of Pho- togrammetry and Remote Sensing, Beijing, pp. 639-644. Gerke, M. and Nyaruhuma, A., 2009. Incorporating scene con straints into the triangulation of airborne oblique images. In: High-Resolution Earth Imaging for Geospatial Information, Vol. XXXVIII, International Archives of Photogrammetry and Re mote Sensing, Hannover. Hartley, R. I. and Zisserman, A., 2004. Multiple View Geometry in Computer Vision. Second edn, Cambridge University Press. Heinrichs, M., Hellwich, O. and Rodehorst, V., 2007. Efficient Semi-Global Matching for trinocular stereo. In: IAPRS, Vol. 36, pp. 185-190. Part 3-W49A (PIA conference. Munich, Germany). Hirschmüller, H., 2008. Stereo processing by Semi-Global Mat ching and Mutual Information. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(2), pp. 328-341. Hirschmüller, H., Schölten, F. and Hirzinger, G., 2005. Stereo vision based reconstruction of huge urban areas from an airborne pushbroom camera (HRSC). In: Lecture Notes in Computer Sci ence: Pattern Recognition, Proceedings of the 27th DAGM Sym posium, Vol. 3663, Springer. Berlin, pp. 58-66. Höhle, J., 2008. Photogrammetric measurements in oblique aerial images. Photogrammetrie Fernerkundung Geoinformation l.pp. 7-14. Lemmen, M., Lemmen, C. and Wubbe, M., 2007. Pictometry : potentials for land administration. In: Proceedings of the 6th FIG regional conference, International Federation of Surveyors (FIG). Lowe, D. G., 2004. Distinctive image features from scale- invariant keypoints. International Journal of Computer Vision 60(2), pp. 91-110. Mishra, P, Ofek, E. and Kimchi, G., 2008. Validation of vec tor data using oblique images. In: Proceedings of the 16th ACM SIGSPATIAL International conference on advances in Ge ographic Information Systems, ACM, Irvine, California. Oram, D., 2001. Rectification for any epipolar geometry. In: Proceedings of the 12th British Machine Vision Conference (BMVC). Petrie, G. and Walker, A. S., 2007. Airborne digital imaging tech nology: a new overview. The Photogrammetric Record 22(119), pp. 203-225. Seitz, S. M., Curless, B., Diebel, J., Scharstein, D. and Szeliski, R., 2006. A comparison and evaluation of multi-view stereo re construction algorithms. CVPR 1, pp. 519-526. Wang, M., Bai, H. and Hu, F., 2008. Automatic texture acqui sition for 3d model using oblique aerial images. In: Intelligent Networks and Intelligent Systems, International Workshop on, pp. 495^498. ’http://www.aspa29.dsl.pipex.com (accessed 15 March 2009) 82

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