Abstract
Most of the image registration/matching methods are applicable to images acquired by either identical or similar sensors from various positions. Simpler techniques assume some object space relationship between sensor reference points, such as near parallel image planes, certain overlap and comparable radiometric characteristics. More robust methods allow for larger variations in image orientation and texture, such as the Scale-Invariant Feature Transformation (SIFT), a highly robust technique widely used in computer vision. The use of SIFT, however, is quite limited in mapping so far, mainly, because most of the imagery are acquired from airborne/spaceborne platforms, and, consequently, the image orientation is better known, presenting a less general case for matching. The motivation for this study is to look at the feasibility of a particular case of matching between different image domains. In this investigation, the co-registration of satellite imagery and LiDAR intensity data is addressed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abdel-Hakim, A.E., Farag, A.A.: CSIFT: A SIFT Descriptor with Color Invariant Characteristics. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1978–1983 (2006)
Abedini, A., Hahn, M., Samadzadegan, F.: An Investigation into the Registration of LiDAR Intensity Data and Aerial Images using the SIFT Approach. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII Part B1 WG I/2, 169 (2008)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: SURF: Speeded Up Robust Features. Computer Vision and Image Understanding (CVIU) 110(3), 346–359 (2008)
Brown, M., Lowe, D.G.: Invariant feature from interest point groups. In: British Machine Vision Conference, Cardiff, Wales, pp. 656–665 (2002)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-Based Object Tracking. IEEE Transcations on Pattern Analysis and Matchine Intelligence 25(5), 564–577
Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM 24, 381–395 (1981)
Habib, A.F., Bang, K.I., Aldelgawy, M., Shin, S.W., Kim, K.O.: Integration of Photogrammetric and LiDAR Data in a Multi-Primitive Triangulation Procedure. In: Proceedings of ASPRS 2007 Annual Conference - Identifying Geospatial Solutions, Tampa, FL, May 7-11, CD-ROM (2007)
Habib, A.F., Ghanma, M.S., Tait, M.: Integration of LiDAR and Photogrammetry for Close Range Applications. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences XXXV, Part B5 (2004)
Habib, A.F., Ghanma, M.S., Morgan, M.F., Mitishita, E.: Integration of Laser and Photogrammetric Data for Calibration Purpose. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences XXXV, part B2, 170 (2004)
Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation for local image descriptors. In: Proceedings International Conferences on Computer Vision, Washington DC, pp. 506–513 (2004)
Kim, C., Habib, A.: Object-Based Integration of Photogrammetric and LiDAR Data for Automated Generation of Complex Polyhedral Building Models. Sensors 9(7), 5679–5701 (2009)
Li, Q.L., Wang, G.Y., Liu, J.G., Chen, S.B.: Robust Scale-Invariant Feature Matching for Remote Sensing Image Registration. IEEE Geoscience And Remote Sensing Letters 6(2), 287–291 (2009)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings International Conferences on Computer Vision, Corfu, Greece, pp. 1150–1157 (1999)
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)
Reddy, B.S., Chatterji, B.N.: An FFT-Based Technique for Translation, Rotation, and Scale-Invariant Image Registration. IEEE Transactions On Image Processing 5(8), 1266–1271 (1996)
Toth, C.K., Ju, H., Grejner-Brzezinska, D.A.: Experiences with using SIFT for Mulitple Image Domain Matching. In: Proceedings of ASPRS Annual Conference, San Diego, CA, April 26-30, CD-ROM (2010)
Wolberg, G., Zokai, S.: Robust Image Registration using Log-Polar Transform. In: Proceedings 2000 International Conference on Image Processing, vol. 1, pp. 493–496 (2000)
Yi, Z., Cao, Z.G., Yang, X.: Multi-spectral remote image registration based on SIFT. Electronic Letter 44(2), 107–108 (2008)
Zokai, S., Wolberg, G.: Image Registration using Log-Polar Mappings for Recovery of Large-Scale Similarity and Projective Transformations. IEEE Transactions On Image Processing 14(10), 1422–1434 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Toth, C., Ju, H., Grejner-Brzezinska, D. (2011). Matching between Different Image Domains. In: Stilla, U., Rottensteiner, F., Mayer, H., Jutzi, B., Butenuth, M. (eds) Photogrammetric Image Analysis. PIA 2011. Lecture Notes in Computer Science, vol 6952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24393-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-24393-6_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24392-9
Online ISBN: 978-3-642-24393-6
eBook Packages: Computer ScienceComputer Science (R0)