Semi-automated registration of close-range hyperspectral scans using oriented digital camera imagery and a 3D model

Photogrammetric Record

Volumn 29, Issue 145, 2014, Pages 10-29

  Sima, Aleksandra A ^a,c   Buckley, Simon J ^a   Kurz, Tobias H ^a   Schneider, Danilo ^b  

^a Centre for Integrated PetroleumResearch (Uni CIPR)   (Norway)

^b DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

^c FLEMISH INSTITUTE FOR TECHNOLOGICAL RESEARCH VITO   (Belgium)

Author keywords

Band selection; Fusion; Hyperspectral; Panoramic; SIFT; Terrestrial laser scanning

Indexed keywords

AUTOMATION; DATA INTEGRATION; DIGITAL CAMERAS; FUSION REACTIONS; OPTICAL RADAR; SPECTROSCOPY; VIDEO CAMERAS;

BAND SELECTION; HYPERSPECTRAL; PANORAMIC; SIFT; TERRESTRIAL LASER SCANNING;

THREE DIMENSIONAL;

AUTOMATION; DATA ACQUISITION; DIGITAL PHOTOGRAMMETRY; GEOMETRY; LASER METHOD; LIDAR; SPATIAL RESOLUTION; SPECTRAL ANALYSIS; THREE-DIMENSIONAL MODELING;

EID: 84897046443     PISSN: 0031868X     EISSN: 14779730     Source Type: Journal    
DOI: 10.1111/phor.12049     Document Type: Article

References (40)

1. Alba, M., Bernardini, G., Giussani, A., Ricci, P. P., Rancoroni, F., Scaioni, M., Valgoi, P. and Zhang, K., 2008. Measurement of dam deformations by terrestrial interferometric techniques. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B1): 133-139.
2. Böhm, J. and Becker, S., 2007. Automatic marker-free registration of terrestrial laser scans using reflectance features. 8th Conference on Optical 3D Measurement Techniques, Zurich, Switzerland. 338-344. http://www.ifp.uni-stuttgart.de/publications/2007/Boehm_marker-free_2007.pdf [Accessed: 14th January 2014].
3. Bradski, G. and Kaehler, A., 2008. Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media, Sebastopol, California, USA. 555 pages.
4. Buckley, S. J., Enge, H., Carlsson, C. and Howell, J. A., 2010. Terrestrial laser scanning for use in virtual outcrop geology. Photogrammetric Record, 25(131): 225-239.
5. Buckley, S. J., Kurz, T. H., Howell, J. A. and Schneider, D., 2013. Terrestrial lidar and hyperspectral data fusion products for geological outcrop analysis. Computers & Geosciences, 54: 249-258.
6. Delponte, E., Isgrò, F., Odone, F. and Verri, A., 2006. SVD-matching using SIFT features. Graphical Models, 68(5-6): 415-431.
7. Du, Q. and Yang, H., 2008. Similarity-based unsupervised band selection for hyperspectral image analysis. IEEE Geoscience and Remote Sensing Letters, 5(4): 564-568.
8. Durda, F., 2012. NTSC Primer. http://nemesis.lonestar.org/reference/internet/web/color/ntsc_primer.html [Accessed: 16th October 2012].
9. Fischler, M. A. and Bolles, R. C., 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6): 381-395.
10. Goetz, A. F. H., Vane, G., Solomon, J. E. and Rock, B. N., 1985. Imaging spectrometry for earth remote sensing. Science, 228(4704): 1147-1153.
11. González-Aguilera, D., Rodríguez-Gonzálvez, P. and Gómez-Lahoz, J., 2009. An automatic procedure for co-registration of terrestrial laser scanners and digital cameras. ISPRS Journal of Photogrammetry and Remote Sensing, 64(3): 308-316.
12. Khan, N. Y., McCane, B. and Wyvill, G., 2011. SIFT and SURF performance evaluation against various image deformations on benchmark dataset. International Conference on Digital Image Computing Techniques and Applications (DICTA 2011), Queensland, Australia. 501-506.
13. Kim, C.-H., Seong, S.-M., Lee, J.-A. and Kim, L.-S., 2003. Winscale: an image-scaling algorithm using an area pixel model. IEEE Transactions on Circuits and Systems for Video Technology, 13(6): 549-553.
14. Kim, D., Min, D. and Sohn, K., 2007. 3D scene reconstruction system with hand-held stereo cameras. 3DTV Conference, Kos, Greece. 1-4.
15. Kruse, F. A., Bedell, R. L., Taranik, J. V., Peppin, W. A., Weatherbee, O. and Calvin, W. M., 2012. Mapping alteration minerals at prospect, outcrop, and drill core scales using imaging spectrometry. International Journal of Remote Sensing, 33(6): 1780-1798.
16. Kullback, S. and Leibler, R. A., 1951. On information and sufficiency. Annals of Mathematical Statistics, 22(1): 79-86.
17. Kurz, T. H., Buckley, S. J., Howell, J. A. and Schneider, D., 2008. Geological outcrop modelling and interpretation using ground based hyperspectral and laser scanning data fusion. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B8): 1229-1234.
18. Kurz, T. H., Buckley, S. J., Howell, J. A. and Schneider, D., 2011. Integration of panoramic hyperspectral imaging with terrestrial lidar data. Photogrammetric Record, 26(134): 212-228.
19. Kurz, T. H., Dewit, J., Buckley, S. J., Thurmond, J. B., Hunt, D. W. and Swennen, R., 2012. Hyperspectral image analysis of different carbonate lithologies (limestone, karst and hydrothermal dolomites): the Pozalagua Quarry case study (Cantabria, north-west Spain). Sedimentology, 59(2): 623-645.
20. Lagüela, S., Armesto, J., Arias, P. and Zakhor, A., 2012. Automatic procedure for the registration of thermographic images with point clouds. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 39(B5): 211-216.
21. Lingua, A., Marenchino, D. and Nex, F., 2009. Performance analysis of the SIFT operator for automatic feature extraction and matching in photogrammetric applications. Sensors, 9(5): 3745-3766.
22. Lowe, D. G., 1999. Object recognition from local scale-invariant features. Proceedings of the 7th IEEE International Conference on Computer Vision, 2: 1150-1157.
23. Lowe, D. G., 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2): 91-110.
24. Martínez-Usó, A., Pla, F., Sotoca, J. M. and García-Sevilla, P., 2007. Clustering-based hyperspectral band selection using information measures. IEEE Transactions on Geoscience and Remote Sensing, 45(12): 4158-4171.
25. May, M., Turner, M. J. and Morris, T., 2010. Scale invariant feature transform: a graphical parameter analysis. Proceedings of the British Machine Vision Conference, Postgraduate Workshop, Aberystwyth, UK, 5(1-5): 11.
26. Meierhold, N., Spehr, M., Schilling, A., Gumhold, S. and Maas, H.-G., 2010. Automatic feature matching between digital images and 2D representations of a 3D laser scanner point cloud. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(5): 446-451.
27. Nieto, J. I., Monteiro, S. T. and Viejo, D., 2010. 3D Geological modelling using laser and hyperspectral data. Proceedings of the IGARSS 2010 Symposium, Honolulu, Hawaii, USA. 4568-4571.
28. Ogawa, Y., Shimada, N. and Shirai, Y., 2007. Environmental mapping for mobile robot by tracking SIFT feature points using trinocular vision. Proceedings of the SICE Annual Conference, Kagawa, Japan. 1996-2001.
29. Ostiak, P., 2006. Implementation of HDR panorama stitching algorithm. 10th Central European Seminar on Computer Graphics for Students (CESCG), Castá-Papiernicka, Slovakia. 8 pages. http://www.cescg.org/CESCG-2006/papers/Szczecin-Ostiak-Piotr.pdf [Accessed: 14th January 2014].
30. Pfeifer, N., Dorninger, P., Haring, A. and Fan, H., 2007. Investigating terrestrial laser scanning intensity data: quality and functional relations. 8th Conference on Optical 3-D Measurement Techniques, Zurich, Switzerland. 328-337.
31. Schneider, D. and Maas, H.-G., 2006. A geometric model for linear-array-based terrestrial panoramic cameras. Photogrammetric Record, 21(115): 198-210.
32. Schneider, D. and Maas, H.-G., 2007. Integrated bundle adjustment with variance component estimation - fusion of terrestrial laser scanner data, panoramic and central perspective image data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(3/W52): 373-378.
33. Shannon, C. E., 1948. A mathematical theory of communication. The Bell System Technical Journal, 27(3 and 4): 379-423 and 623-656.
34. Sima, A., Buckley, S. J., Kurz, T. H. and Schneider, D., 2012. Semi-automatic integration of panoramic hyperspectral imagery with photorealistic lidar models. Photogrammetrie, Fernerkundung, Geoinformation, 4: 443-454.
35. Sima, A. A. and Buckley, S. J., 2013. Optimizing SIFT for matching of short wave infrared and visible wavelength images. Remote Sensing, 5(5): 2037-2056.
36. Slocum, T. A., McMaster, R. B., Kessler, F. C. and Howard, H. H., 2008. Thematic Cartography and Geovisualization. Third Edition. Prentice Hall, Upper Saddle River, New Jersey, USA. 576 pages.
37. Su, H., Du, P. and Du, Q., 2012. Semi-supervised dimensionality reduction using orthogonal projection divergence-based clustering for hyperspectral imagery. Optical Engineering, 51(11): 111715-1-111715-8.
38. van der Meer, F. D. and de Jong, S. M., 2002. Imaging Spectrometry: Basic Principles and Prospective Applications (Remote Sensing and Digital Image Processing 4). Springer, Dordrecht, the Netherlands. 425 pages.
39. Weinmann, M., Hoegner, L., Leitloff, J., Stilla, U., Hinz, S. and Jutzi, B., 2012. Fusing passive and active sensed images to gain infrared-textured 3D models. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 39(B1): 71-76.
40. Wessel, B., Huber, M. and Roth, A., 2007. Registration of near real-time SAR images by image-to-image matching. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(3/W49B): 179-184.