Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-77c89778f8-n9wrp Total loading time: 0 Render date: 2024-07-21T00:36:01.086Z Has data issue: false hasContentIssue false

3 - Survey of image registration methods

from PART I - The Importance of Image Registration for Remote Sensing

Published online by Cambridge University Press:  03 May 2011

By
Roger D. Eastman ,
Nathan S. Netanyahu  and
Jacqueline Le Moigne
Edited by
Jacqueline Le Moigne ,
Nathan S. Netanyahu  and
Roger D. Eastman
Roger D. Eastman
Affiliation:
Loyola University, Maryland
Nathan S. Netanyahu
Affiliation:
University of Maryland, Maryland
Jacqueline Le Moigne
Affiliation:
NASA Goddard Space Flight Center, Maryland
Jacqueline Le Moigne
Affiliation:
NASA-Goddard Space Flight Center
Nathan S. Netanyahu
Affiliation:
Bar-Ilan University, Israel and University of Maryland, College Park
Roger D. Eastman
Affiliation:
Loyola University Maryland
Get access

Summary

Introduction

Automatic image registration, bringing two images into alignment by computing a moderately small set of transformation parameters, might seem a well-defined, limited problem that should have a clear, universal solution. Unfortunately, this is far from the state of the art. With a wide spectrum of applications to diverse categories of data, image registration has evolved into a complex and challenging problem that admits many solution strategies. The growing availability of digital imagery in remote sensing, medicine, and numerous other areas has driven a substantial increase in research in image registration over the past 20 years. This growth in research stems from both this increasing diversity in image sources, as image registration is applied to new instruments like hyperspectral sensors in remote sensing and medical imaging scanners in medicine, and new algorithmic principles, as researchers have applied techniques such as wavelet-based features, information theoretic metrics and stochastic numeric optimization.

This chapter surveys the diversity of image registration strategies applied to remote sensing. The objectives of the survey are to explain basic concepts used in the literature, review selected algorithms, give an overall framework to categorize and compare algorithms, and point the reader to the literature for more detailed explanations. While manual and semi-manual approaches are still important in remote sensing, our primary intent is to review research approaches for building fully automatic and operational registration systems. Following the survey article by Brown (1992), we review an algorithm by considering the basic principles from which it is constructed.

Type
Chapter
Information
Image Registration for Remote Sensing , pp. 35 - 76
Publisher: Cambridge University Press
Print publication year: 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdelfattah, R. and Nicolas, J. M. (2005). InSAR image co-registration using the Fourier-Mellin transform. International Journal of Remote Sensing, 26(13), 2865–2876.CrossRefGoogle Scholar
Ackerman, F. (1984). Digital image correlation: Performance and potential application in photogrammetry. The Photogrammetric Record, 11(64), 429–439.CrossRefGoogle Scholar
Alt, H., Aichholzer, O., and Rote, G. (1994). Matching shapes with a reference point. In Proceedings of the Tenth Annual ACM Symposium on Computational Geometry, Stony Brook, NY, pp. 85–91.CrossRef
Alt, H., Fuchs, U., Rote, G., and Weber, G. (1996). Matching convex shapes with respect to the symmetric difference. In Proceedings of the Fourth Annual European Symposium on Algorithms. London: Springer-Verlag, pp. 320–333.Google Scholar
Althof, R. J., Wind, M. G., and Dobbins, J. T. I. (1997). A rapid and automatic image registration algorithm with subpixel accuracy. IEEE Transactions on Medical Imaging, 16(2), 308–316.CrossRefGoogle ScholarPubMed
Andrus, J., Campbell, C., and Jayroe, R. (1975). Digital image registration method using boundary maps. IEEE Transactions on Computers, 24(9), 935–940.CrossRefGoogle Scholar
Arevalo, V. and Gonzalez, J. (2008a). An experimental evaluation of non-rigid registration techniques on QuickBird satellite imagery. International Journal of Remote Sensing, 29(2), 513–527.CrossRefGoogle Scholar
Arevalo, V. and Gonzalez, J. (2008b). Improving piecewise linear registration of high-resolution satellite images through mesh optimization. IEEE Transactions on Geoscience and Remote Sensing, 46(11), 3792–3803.CrossRefGoogle Scholar
Armston, J., Danaher, T., Goulevitch, B., and Byrne, M. (2002). Geometric correction of Landsat MSS, TM, and ETM+ imagery for mapping of woody vegetation cover and change detection in Queensland. In Proceedings of the Eleventh Australasian Remote Sensing and Photogrammetry Conference, Brisbane, Queensland, Australia, pp. 1–23.
Arya, K., Gupta, P., Kalra, P., and Mitra, P. (2007). Image registration using robust M-estimators. Pattern Recognition Letters, 28, 1957–1968.CrossRefGoogle Scholar
Baillarin, S., Bouillon, A., Bernard, M., and Chikhi, M. (2005). Using a three dimensional spatial database to orthorectify automatically remote sensing images. In Proceedings of the ISPRS Hangzhou 2005 Workshop on Service and Application of Spatial Data Infrastructure, Hangzhou, China, pp. 89–93.
Baker, S. and Matthews, I. (2001). Equivalence and efficiency of image alignment algorithms. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, pp. 1090–1097.CrossRef
Bardera, A., Feixas, M., and Boada, I. (2004). Normalized similarity measures for medical image registration. In Proceedings of the SPIE International Symposium on Medical Imaging, San Diego, CA, pp. 108–118.CrossRef
Barnea, D. J. and Silverman, H. F. (1972). A class of algorithms for fast digital image registration. IEEE Transactions on Computing, 21, 179–186.CrossRefGoogle Scholar
Bartoli, A. (2008). Groupwise geometric and photometric direct image registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(12), 2098–2108.CrossRefGoogle ScholarPubMed
Bentoutou, Y., Taleb, N., Kpalma, K., and Ronsin, J. (2005). An automatic image registration for applications in remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 43(9), 2127–2137.CrossRefGoogle Scholar
Brovelli, M. A., Crespi, M., Fratarcangeli, F., Giannone, F., and Realin, E. (2006). Accuracy assessment of high resolution satellite imagery by leave-one-out method. In Proceedings of the Seventh International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Science, Lisbon, Portugal, pp. 533–542.
Brown, L. G. (1992). A survey of image registration techniques. ACM Computing Surveys, 24(4), 325–376.CrossRefGoogle Scholar
Bruzzone, L. and Cossu, R. (2003). An adaptive approach to reducing registration noise effects in unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing, 41(11), 2455–2465.CrossRefGoogle Scholar
Buiten, H. J. and Putten, B. (1997). Quality assessment of remote sensing image registration: Analysis and testing of control point residuals. ISPRS Journal of Photogrammetry and Remote Sensing, 52, 57–73.CrossRefGoogle Scholar
Cain, S., Hayat, M., and Armstrong, E. (2001). Projection-based image registration in the presence of fixed-pattern noise. IEEE Transactions on Image Processing, 10(12), 1860–1872.CrossRefGoogle ScholarPubMed
Cariou, C. and Chehdi, K. (2008). Automatic georeferencing of airborne pushbroom scanner images with missing ancillary data using mutual information. IEEE Transactions on Geoscience and Remote Sensing, 46(5), 1290–1300.CrossRefGoogle Scholar
Carr, J. L., Mangolini, M., Pourcelot, B., and Baucom, A. W. (1997). Automated and robust image geometry measurement techniques with application to meteorological satellite imaging. In Proceedings of the CESDIS Image Registration Workshop, NASA Goddard Space Flight Center, Greenbelt, MD, pp. 89–99.
Carrion, D., Gianinetto, M., and Scaioni, M. (2002). GEOREF: A software for improving the use of remote sensing images in environmental applications. In Proceedings of the First Biennial Meeting of the International Environmental Modelling and Software Society, Vol. 3, Lugano, Switzerland, pp. 360–365.
Ceccarelli, M., Bisceglie, M., Galdi, C., Giangregorio, G., and Liberata, U. S. (2008). Image registration using non-linear diffusion. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, pp. 220–223.CrossRef
Chen, H. M., Arora, M. K., and Varshney, P. K. (2003). Mutual information-based image registration for remote sensing data. International Journal of Remote Sensing, 24(18), 3701–3706.CrossRefGoogle Scholar
Chen, H., Varshney, P. K., and Arora, M. K. (2003). Performance of mutual information similarity measure for registration of multitemporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 41(11), 2445–2454.CrossRefGoogle Scholar
Chen, L.-C. and Lee, L.-H. (1992). Progressive generation of control frame works for image registration. Photogrammetric Engineering & Remote Sensing, 58(9), 1321–1328.Google Scholar
Chen, Q., Defrise, M., and Deconinck, F. (1994). Symmetric phase-only matched filtering of Fourier-Mellin transforms for images registration and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(12), 1156–1168.CrossRefGoogle Scholar
Chen, T. and Huang, T. S. (2007). Optimizing image registration by mutually exclusive scale components. In Proceedings of the IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, pp. 1–8.CrossRef
Chew, L. P., Goodrich, M. T., Huttenlocher, D. P., Kedem, K., Kleinberg, J. M., and Kravets, D. (1997). Geometric pattern matching under Euclidean motion. Computational Geometry Theory and Applications, 7, 113–124.CrossRefGoogle Scholar
Cho, M. and Mount, D. M. (2005). Improved approximation bounds for planar point pattern matching. In Proceedings of the Ninth Workshop on Algorithms and Data Structures, Waterloo, Canada; Lecture Notes in Computer Science, Springer-Verlag, Vol. 3608, pp. 432–443.Google Scholar
Choi, V. and Goyal, N. (2006). An efficient approximation algorithm for point pattern matching under noise. In Proceedings of the Seventh Latin American Theoretical Informatics Symposium, Valdivia, Chile; Lecture Notes in Computer Science, Springer-Verlag, Vol. 3887, pp. 298–310.Google Scholar
Cole-Rhodes, A., Johnson, K., Moigne, J., and Zavorin, I. (2003). Multiresolution registration of remote sensing images using stochastic gradient. IEEE Transactions on Image Processing, 12(12), 1495–1511.CrossRefGoogle ScholarPubMed
Dai, X. L. and Khorram, S. (1998). The effects of image misregistration on the accuracy of remotely sensed change detection. IEEE Transactions on Geoscience and Remote Sensing, 36(5), 1566–1577.Google Scholar
Dai, X. L. and Khorram, S. (1999). A feature-based image registration algorithm using improved chain-code representation combined with invariant moments. IEEE Transactions on Geoscience and Remote Sensing, 37(5), 2351–2362.Google Scholar
Dare, P. and Dowman, I. (2001). An improved model for automatic feature-based registration of SAR and SPOT images. ISPRS Journal of Photogrammetry and Remote Sensing, 56(1), 13–28.CrossRefGoogle Scholar
Castro, E. and Morandi, C. (1987). Registration of translated and rotated images using finite Fourier transforms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(5), 700–703.CrossRefGoogle ScholarPubMed
Dewdney, A. K. (1978). Analysis of a steepest-descent image-matching algorithm. Pattern Recognition, 10(1), 31–39.CrossRefGoogle Scholar
Dowson, N. and Bowden, R. (2006). A unifying framework for mutual information methods for use in non-linear optimisation. In Proceedings of the Ninth European Conference on Computer Vision, Graz, Austria; Lecture Notes in Computer Science, Springer, Vol. 3951, pp. 365–378.Google Scholar
Dowson, N., Kadir, T., and Bowden, R. (2008). Estimating the joint statistics of images using nonparametric windows with application to registration using mutual information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(10), 1841–1857.CrossRefGoogle ScholarPubMed
Dufournaud, Y., Schmid, C., and Horaud, R. (2004). Image matching with scale adjustment. Computer Vision and Image Understanding, 93(2), 175–194.CrossRefGoogle Scholar
Eastman, R. D., Moigne, J., and Netanyahu, N. S. (2007). Research issues in image registration for remote sensing. In Proceedings of the IEEE Computer Vision and Pattern Recognition Workshop on Image Registration and Fusion, Minneapolis, MN, pp. 3233–3240.CrossRef
Emery, W., Baldwin, D., and Matthews, D. (2003). Maximum cross correlation automatic satellite image navigation and attitude corrections for open-ocean image navigation. IEEE Transactions on Geoscience and Remote Sensing, 41(1), 33–42.CrossRefGoogle Scholar
Eugenio, F. and Marques, F. (2003). Automatic satellite image georeferencing using a contour-matching approach. IEEE Transactions on Geoscience and Remote Sensing, 41(12), 2869–2880.CrossRefGoogle Scholar
Faugeras, O. (1993). Three-Dimensional Computer Vision. Boston, MA: MIT Press.Google Scholar
Felicsimo, A. M., Cuartero, A., and Polo, M. E. (2006). Analysis of homogeneity and isotropy of spatial uncertainty by means of GPS kinematic check lines and circular statistics. In Proceedings of the Seventh International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Science, Lisbon, Portugal, pp. 85–90.
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.CrossRef
Fitzpatrick, J., West, J. B., and Maurer, C. R. (1998). Predicting error in rigid-body point-based registration. IEEE Transactions on Medical Imaging, 17(5), 694–702.CrossRefGoogle ScholarPubMed
Flusser, J. and Suk, T. (1994). A moment-based approach to registration of images with affine geometric distortion. IEEE Transactions on Geoscience and Remote Sensing, 32(2), 382–387.CrossRefGoogle Scholar
Fonseca, L. M. G. and Manjunath, B. S. (1996). Registration techniques for multisensor remotely sensed imagery. Photogrammetric Engineering & Remote Sensing, 62(9), 1049–1056.Google Scholar
Foroosh, H., Zerubia, J. B., and Berthod, M. (2002). Extension of phase correlation to subpixel registration. IEEE Transactions on Image Processing, 11(3), 188–200.CrossRefGoogle ScholarPubMed
Förstner, W. and Gülch, E. (1987). A fast operator for detection and precise location of distinct points, corners, and centers of circular features. In Proceedings of the ISPRS Intercommission Workshop on Fast Processing of Photogrammetric Data, Interlaken, Switzerland, pp. 281–385.
Gavrilov, M., Indyk, P., Motwani, R., and Venkatasubramanian, S. (2004). Combinatorial and experimental methods for approximate point pattern matching. Algorithmica, 38(1), 59–90.CrossRefGoogle Scholar
Georgescu, B. and Meer, P. (2004). Point matching under large image deformations and illumination changes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6), 674–688.CrossRefGoogle ScholarPubMed
Goodrich, M. T., Mitchell, J. S. B., and Orletsky, M. W. (1999). Approximate geometric pattern matching under rigid motions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4), 371–379.CrossRefGoogle Scholar
Goshtasby, A. (1988). Registration of images with geometric distortions. IEEE Transactions on Geoscience and Remote Sensing, 26(1), 60–64.CrossRefGoogle Scholar
Goshtasby, A. (2005). 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications. Hoboken, NJ: John Wiley and Sons.Google Scholar
Goshtasby, A. and Stockman, G. C. (1985). Point pattern matching using convex hull edges. IEEE Transactions on Systems, Man, and Cybernetics, 15(5), 631–637.CrossRefGoogle Scholar
Goshtasby, A., Stockman, G., and Page, C. (1986). A region-based approach to digital image registration with subpixel accuracy. IEEE Transactions on Geoscience and Remote Sensing, 24(3), 330–399.Google Scholar
Govindu, V. and Shekhar, C. (1999). Alignment using distributions of local geometric properties. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(10), 1031–1043.CrossRefGoogle Scholar
Grodecki, J. and Lutes, J. (2005). IKONOS geometric calibrations. Technical report, Space Imaging.
Growe, S. and Tönjes, R. (1997). A knowledge based approach to automatic image registration. In Proceedings of the International Conference on Image Processing, Washington, DC, pp. 228–231.CrossRef
Gruen, A. (1985). Adaptive least squares correlation: A powerful image matching technique. South African Journal of Photogrammetry, Remote Sensing and Cartography, 14, 175–187.Google Scholar
Gupta, N. (2007). A VLSI architecture for image registration in real time. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 15(9), 981–989.CrossRefGoogle Scholar
Habib, A. F. and Alruzouq, R. I. (2004). Line-based modified iterated Hough transform for automatic registration of multi-source imagery. The Photogrammetric Record, 19(105), 5–21.CrossRefGoogle Scholar
Habib, A. F. and Al-Ruzouq, R. (2005). Semi-automatic registration of multi-source satellite imagery with varying geometric resolutions. Photogrammetric Engineering & Remote Sensing, 71(3), 325–332.CrossRefGoogle Scholar
Hagedoorn, M. and Veltkamp, R. C. (1999). Reliable and efficient pattern matching using an affine invariant metric. International Journal of Computer Vision, 31(2–3), 103–115.CrossRefGoogle Scholar
Harris, C. and Stephens, M. (1988). A combined corner and edge detector. In Proceedings of the Fourth Alvey Vision Conference, Manchester, UK, pp. 147–151.CrossRef
Hasler, D., Sbaiz, L., Susstrunk, S., and Vetterli, M. (2003). Outlier modeling in image matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(3), 301–315.CrossRefGoogle Scholar
He, Y., Hamza, A. B. and Krim, H. (2003). A generalized divergence measure for robust image registration. IEEE Transactions on Signal Processing, 51(5), 1211–1220.Google Scholar
Heffernan, P. J. and Schirra, S. (1994). Approximate decision algorithms for point set congruence. Computational Geometry Theory and Applications, 4(3), 137–156.CrossRefGoogle Scholar
Hong, G. and Zhang, Y. (2008). A comparative study on radiometric normalization using high resolution satellite images. International Journal of Remote Sensing, 29(2), 425–438.CrossRefGoogle Scholar
Hong, S. H., Jung, H. S., and Won, J. S. (2006). Extraction of ground control points (GCPs) from synthetic aperture radar images and SRTM DEM. International Journal of Remote Sensing, 27(18), 3813–3829.CrossRefGoogle Scholar
Hsieh, J.-W., Liao, H.-Y. M., Fan, K.-C., Ko, M.-T., and Hung, Y.-P. (1997). Image registration using a new edge-based approach. Computer Vision and Image Understanding, 67(2), 112–130.CrossRefGoogle Scholar
Huseby, R. B., Halck, O. M., and Solberg, R. (2005). A model-based approach for geometrical correction of optical satellite images. International Journal of Remote Sensing, 26(15), 3205–3223.CrossRefGoogle Scholar
Huttenlocher, D. P. and Rucklidge, W. J. (1993). A multi-resolution technique for comparing images using the Hausdorff distance. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New York, NY, pp. 705–706.CrossRef
Huttenlocher, D. P., Kedem, K., and Sharir, M. (1993a). The upper envelope of Voronoi surfaces and its applications. Discrete and Computational Geometry, 9, 267–291.CrossRefGoogle Scholar
Huttenlocher, D. P., Klanderman, G. A., and Rucklidge, W. J. (1993b). Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9), 850–863.CrossRefGoogle Scholar
Igbokwe, J. I. (1999). Geometrical processing of multi-sensoral multi-temporal satellite images for change detection studies. International Journal of Remote Sensing, 20(6), 1141–1148.CrossRefGoogle Scholar
Im, J., Rhee, J., Jensen, J. R., and Hodgson, M. E. (2007). An automated binary change detection model using a calibration approach. Remote Sensing of Environment, 106(1), 89–105.CrossRefGoogle Scholar
Inglada, J. and Giros, A. (2004). On the possibility of automatic multisensor image registration. IEEE Transactions on Geoscience and Remote Sensing, 41(10), 2104–2120.CrossRefGoogle Scholar
Inglada, J., Muron, V., Pichard, D., and Feuvrier, T. (2007). Analysis of artifacts in subpixel remote sensing image registration. IEEE Transactions on Geoscience and Remote Sensing, 45(1), 254–264.CrossRefGoogle Scholar
Irani, M. and Anandan, P. (1999). About direct methods. In Proceedings of the IEEE ICCV Workshop on Vision Algorithms, Corfu, Greece, pp. 267–277.
Irani, M. and Peleg, S. (1991). Improving resolution by image registration. CVGIP: Graphical Models and Image Processing, 53(3), 231–239.Google Scholar
Iwasaki, A. and Fujisada, H. (2005). ASTER geometric performance. IEEE Transactions on Geoscience and Remote Sensing, 43(12), 2700–2706.CrossRefGoogle Scholar
Jenkinson, M. and Smith, S. (2001). A global optimisation method for robust affine registration of brain images. Medical Image Analysis, 5, 142–156.CrossRefGoogle ScholarPubMed
Jovanovic, V. M., Bull, M. A., Smyth, M. M., and Zong, J. (2002). MISR in-flight camera geometric model calibration and georectification performance. IEEE Transactions on Geoscience and Remote Sensing, 40(7), 1512–1519.CrossRefGoogle Scholar
Kaneko, S., Satohb, Y., and Igarashia, S. (2003). Using selective correlation coefficient for robust image registration. Pattern Recognition, 36(5), 1165–1173.CrossRefGoogle Scholar
Ke, Y. and Sukthankar, R. (2004). PCA-SIFT: A more distinctive representation for local image descriptors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, pp. 506–513.
Kedem, K. and Yarmovski, Y. (1996). Curve based stereo matching using the minimum Hausdorff distance. In Proceedings of the Twelfth Annual ACM Symposium on Computational Geometry, Philadelphia, PA, pp. C15–C18.CrossRef
Keller, Y. and Averbuch, A. (2006). Multisensor image registration via implicit similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(5), 794–801.CrossRefGoogle ScholarPubMed
Keller, Y., Shkolnisky, Y., and Averbuch, A. (2005). The angular difference function and its application to image registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9), 969–976.CrossRefGoogle ScholarPubMed
Kelman, A., Sofka, M., and Stewart, C. V. (2007). Keypoint descriptors for matching across multiple image modalities and non-linear intensity variations. In Proceedings of the IEEE Computer Vision and Pattern Recognition Workshop on Image Registration and Fusion, Minneapolis, MN, pp. 1–7.CrossRef
Kennedy, R. E. and Cohen, W. B. (2003). Automated designation of tie-points for image-to-image coregistration. International Journal of Remote Sensing, 24(17), 3467–3490.CrossRefGoogle Scholar
Kern, J. and Pattichis, M. (2007). Robust multispectral image registration using mutual-information models. IEEE Transactions on Geoscience and Remote Sensing, 45(5), 1494–1505.CrossRefGoogle Scholar
Kim, J. and Fessler, J. A. (2004). Intensity-based image registration using robust correlation coefficients. IEEE Transactions on Medical Imaging, 23(11), 1430–1444.CrossRefGoogle ScholarPubMed
Kirby, N. E., Cracknell, A. P., Monk, J. G. C., and Anderson, J. A. (2006). The automatic alignment and mosaic of video frames from the variable interference filter imaging spectrometer. International Journal of Remote Sensing, 27(21), 4885–4898.CrossRefGoogle Scholar
Klein, S., Pluim, J. P. W., Staring, M., and Viergever, M. A. (2009). Adaptive stochastic gradient descent optimisation for image registration. International Journal of Computer Vision, 81(3), 227–239.CrossRefGoogle Scholar
Klein, S., Staring, M., and Pluim, J. P. W. (2007). Evaluation of optimization methods for nonrigid medical image registration using mutual information and B-splines. IEEE Transactions on Image Processing, 16(12), 2879–2890.CrossRefGoogle ScholarPubMed
Knops, Z. F., Maintz, J. B. A., Viergever, M. A., and Pluim, J. P. W. (2006). Normalized mutual information based registration using k-means clustering and shading correction. Medical Image Analysis, 10(3), 432–439.CrossRefGoogle ScholarPubMed
Köhn, A., Drexl, J., Ritter, F., König, M., and Peitgen, H. O. (2006). GPU accelerated image registration in two and three dimensions. In Proceedings of Bildverarbeitung für die Medizin 2006. Hamburg: Springer Verlag, pp. 261–265.CrossRef
Krupnik, A. (2003). Accuracy prediction for ortho-image generation. The Photogrammetric Record, 18(101), 41–58.CrossRefGoogle Scholar
Lazaridis, G. and Petrou, M. (2006). Image registration using the Walsh transform. IEEE Transactions on Image Processing, 15(8), 2343–2357.CrossRefGoogle ScholarPubMed
Moigne, J., Campbell, W., Cromp, R., Branch, A., and Center, N. (2002). An automated parallel image registration technique based on the correlation of wavelet features. IEEE Transactions on Geoscience and Remote Sensing, 40(8), 1849–1864.CrossRefGoogle Scholar
Moigne, J., Cole-Rhodes, A., Johnson, K., Morisette, J., Netanyahu, N. S., Eastman, R., Stone, H., and Zavorin, I. (2003). Earth science imagery registration. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France, pp. 161–163.
Moigne, J., Morisette, J., Cole-Rhodes, A., Johnson, K., Netanyahu, N. S., Eastman, R., Stone, H., Zavorin, I., and Jain, P. (2004). A study of the sensitivity of automatic image registration algorithms to initial conditions. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Anchorage, Alaska, pp. 1390–1393.
Moigne, J., Netanyahu, N. S., Masek, J. G., Mount, D., Goward, S., and Honzak, M. (2000). Geo-registration of Landsat data by robust matching of wavelet features. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Honolulu, Hawaii, pp. 1610–1612.
Lee, D. S., Storey, J. C., Choate, M. J., and Hayes, R. W. (2004). Four years of Landsat-7 on-orbit geometric calibration and performance. IEEE Transactions on Geoscience and Remote Sensing, 42(12), 2786–2795.CrossRefGoogle Scholar
Leprince, S., Barbot, S., Ayoub, F., and Avouac, J. (2007). Automatic and precise orthorectification, coregistration, and subpixel correlation of satellite images, application to ground deformation measurements. IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1529–1558.CrossRefGoogle Scholar
Lester, H. and Arridge, S. R. (1999). A survey of hierarchical non-linear medical image registration. Pattern Recognition, 32(1), 129–149.CrossRefGoogle Scholar
Li, H., Manjunath, B., and Mitra, S. (1995). Multisensor image fusion using the wavelet transform. CVGIP: Graphical Models and Image Processing, 57(3), 235–245.Google Scholar
Li, Q., Wang, G., Liu, J., and Chen, S. (2009). Robust scale-invariant feature matching for remote sensing image registration. IEEE Geoscience and Remote Sensing Letters, 6(2), 287–291.Google Scholar
Lowe, D. (1999). Object recognition from local scale-invariant features. In Proceedings of the IEEE International Conference on Computer Vision, Corfu, Greece, Vol. 2, pp. 1150–1157.CrossRef
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 20(2), 91–110.CrossRefGoogle Scholar
Lucas, B. D. and Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In Proceedings of the International Joint Conference on Artificial Intelligence, Vancouver, British Columbia, Canada, pp. 674–679.
Madani, H., Carr, J. L., and Schoeser, C. (2004). Image registration using AutoLand mark. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Anchorage, Alaska, pp. 3778–3781.
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., and Suetens, P. (1997). Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging, 16(2), 187–198.CrossRefGoogle ScholarPubMed
Maes, F., Vandermeulen, D., and Suetens, P. (1999). Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information. Medical Image Analysis, 3(4), 373–386.CrossRefGoogle ScholarPubMed
Maintz, J. and Viergever, M. (1998). A survey of medical image registration. Medical Image Analysis, 2(1), 1–36.CrossRefGoogle ScholarPubMed
McGuire, M. and Stone, H. S. (2000). Techniques for multiresolution image registration in the presence of occlusions. IEEE Transactions on Geoscience and Remote Sensing, 38(3), 1476–1479.CrossRefGoogle Scholar
Mikolajczyk, K. and Schmid, C. (2004). Scale & affine invariant interest point detectors. International Journal of Computer Vision, 60(1), 63–86.CrossRefGoogle Scholar
Mikolajczyk, K. and Schmid, C. (2005). A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10), 1615–1630.CrossRefGoogle ScholarPubMed
Mikolajczyk, K., Tuytelaars, T., Schmid, C., and Zisserman, A. (2005). A comparison of affine region detectors. International Journal of Computer Vision, 65(1–2), 43–72.CrossRefGoogle Scholar
Modersitzki, J. (2004). Numerical Methods for Image Registration. Oxford: Oxford University Press.Google Scholar
Mount, D. M., Netanyahu, N. S., and Moigne, J. (1999). Efficient algorithms for robust feature matching. Pattern Recognition, 32(1), 17–38.CrossRefGoogle Scholar
Netanyahu, N. S., Moigne, J., and Masek, J. G. (2004). Georegistration of Landsat data via robust matching of multiresolution features. IEEE Transactions on Geoscience and Remote Sensing, 42(7), 1586–1600.CrossRefGoogle Scholar
Nies, H., Loffeld, O., Dönmez, B., Hammadi, A. B., and Wang, R. (2008). Image registration of TerraSAR-X data using different information measures. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, pp. 419–422.CrossRef
Orchard, J. (2007). Efficient least squares multimodal registration with a globally exhaustive alignment search. IEEE Transactions on Image Processing, 16(10), 2526–2534.CrossRefGoogle ScholarPubMed
Pan, C., Zhang, Z., Yan, H., Wu, G., and Ma, S. (2008). Multisource data registration based on NURBS description of contours. International Journal of Remote Sensing, 29(2), 569–591.CrossRefGoogle Scholar
Penney, G. P., Weese, J., Little, J. A., Desmedt, P., Hill, D. L. G., and Hawkes, D. J. (1998). A comparison of similarity measures for use in 2-D-3-D medical image registration. IEEE Transactions on Medical Imaging, 17(4), 586–595.CrossRefGoogle ScholarPubMed
Pluim, J. P. W., Maintz, J. B. A., and Viergever, M. A. (2000). Interpolation artefacts in mutual information based image registration. Computer Vision and Image Understanding, 77(2), 211–232.CrossRefGoogle Scholar
Pluim, J., Maintz, J., and Viergever, M. (2003). Mutual-information-based registration of medical images: A survey. IEEE Transactions on Medical Imaging, 22(8), 986–1004.CrossRefGoogle ScholarPubMed
Pluim, J. P., Maintz, J. B. A., and Viergever, M. A. (2004). f-Information measures in medical image registration. IEEE Transactions on Medical Imaging, 23(12), 1506–1518.CrossRefGoogle ScholarPubMed
Poe, G. A., Uliana, E. A., Gardiner, B. A., Rentzell, T. E., and Kunkee, D. B. (2008). Geolocation error analysis of the special sensor microwave imager/sounder. IEEE Transactions on Geoscience and Remote Sensing, 46(4), 913–922.CrossRefGoogle Scholar
Radke, R. J., Andra, S., Al-Kofahi, O., and Roysam, B. (2005). Image change detection algorithms: A systematic survey. IEEE Transactions on Image Processing, 14(3), 294–307.CrossRefGoogle ScholarPubMed
Rajwade, A., Banerjee, A., and Rangarajan, A. (2009). Probability density estimation using isocontours and isosurfaces: Applications to information-theoretic image registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(3), 475–491.CrossRefGoogle ScholarPubMed
Reddy, B. and Chatterji, B. (1996). An FFT-based technique for translation, rotation and scale-invariant image registration. IEEE Transactions on Image Processing, 5(8), 1266–1271.CrossRefGoogle ScholarPubMed
Roche, A., Malandain, G., and Ayache, N. (2000). Unifying maximum likelihood approaches in medical image registration. Special Issue on 3D Imaging. International Journal of Imaging Systems and Technology, 11(1), 71–80.3.0.CO;2-5>CrossRefGoogle Scholar
Rohde, G., Aldroubi, A., and Healy, D. (2008). Interpolation artifacts in sub-pixel image registration. IEEE Transactions on Image Processing, 18(2), 333–345.CrossRefGoogle ScholarPubMed
Roy, D. (2000). The impact of misregistration upon composited wide field of view satellite data and implications for change detection. IEEE Transactions on Geoscience and Remote Sensing, 38(4), 2017–2032.CrossRefGoogle Scholar
Salas, W. A., Boles, S. H., Frolking, S., Xiao, X., and Li, C. (2003). The perimeter/area ratio as an index of misregistration bias in land cover change estimates. International Journal of Remote Sensing, 24(5), 1165–1170.CrossRefGoogle Scholar
Salvado, O. and Wilson, D. L. (2007). Removal of local and biased global maxima in intensity-based registration. Medical Image Analysis, 11(2), 183–196.CrossRefGoogle ScholarPubMed
Salvi, J., Matabosch, C., Fofi, D., and Forest, J. (2006). A review of recent range image registration methods with accuracy evaluation. Image and Vision Computing, 25(5), 578–596.CrossRefGoogle Scholar
Schmid, C., Mohr, R., and Bauckhage, C. (2000). Evaluation of interest point detectors. International Journal of Computer Vision, 37(2), 151–172.CrossRefGoogle Scholar
Sen, M., Hemaraj, Y., Plishker, W., Shekhar, R., and Bhattacharyya, S. S. (2008). Model-based mapping of reconfigurable image registration on FPGA platforms. Journal of Real-Time Image Processing, 3(3), 149–162.CrossRefGoogle Scholar
Sester, M., Hild, H., and Fritsch, D. (1998). Definition of ground-control features for image registration using GIS-data. International Archives of Photogrammetry and Remote Sensing, 32/4, 537–543.Google Scholar
Shams, R., Sadeghi, P., and Kennedy, R. A. (2007). Gradient intensity: A new mutual information based registration method. In Proceedings of the IEEE Computer Vision and Pattern Recognition Workshop on Image Registration and Fusion, Minneapolis, MN, pp. 3249–3256.CrossRef
Shekarforoush, H., Berthod, M., and Zerubia, J. (1996). Subpixel image registration by estimating the polyphase decomposition of cross power spectrum. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, pp. 532–537.CrossRef
Skerl, D., Likar, B., and Pernus, F. (2006). A protocol for evaluation of similarity measures for rigid registration. IEEE Transactions on Medical Imaging, 25(6), 779–791.CrossRefGoogle ScholarPubMed
Stewart, C., Tsai, C., and Roysam, B. (2003). The dual-bootstrap iterative closest point algorithm with application to retinal image registration. IEEE Transactions on Medical Imaging, 22(11), 1379–1394.CrossRefGoogle ScholarPubMed
Stockman, G. C., Kopstein, S., and Benett, S. (1982). Matching images to models for registration and object detection via clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 4(3), 229–241.CrossRefGoogle ScholarPubMed
Stone, H. S., Orchard, M., Chang, E. C., and Martucci, S. (2001). A fast direct Fourier-based algorithm for subpixel segistration of images. IEEE Transactions on Geoscience and Remote Sensing, 39(10), 2235–2243.CrossRefGoogle Scholar
Stone, H. S., Tao, B., and McGuire, M. (2003). Analysis of image registration noise due to rotationally dependent aliasing. Journal of Visual Communication and Image Representation, 14, 114–135.CrossRefGoogle Scholar
Sylvander, S., Henry, P., Bastien-Thiry, C., Meunier, F., and Fuster, D. (2000). VEGETATION geometrical image quality. Société Française de Photogrammétrie et de Télédétection, 159, 59–65.Google Scholar
Theiler, J. P., Galbraith, A. E., Pope, P. A., Ramsey, K. A., and Szymanski, J. J. (2002). Automated coregistration of MTI spectral bands. In Proceedings of the SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, Orlando, FL, Vol. 4725, pp. 314–327.
Thévenaz, P. and Unser, M. (2000). Optimization of mutual information for multiresolution image registration. IEEE Transactions on Image Processing, 9(12), 2083–2099.Google ScholarPubMed
Thévenaz, P., Bierlaire, M., and Unser, M. (2008). Halton sampling for image registration based on mutual information. Sampling Theory in Signal and Image Processing, 7(2), 141–171.Google Scholar
Thévenaz, P., Ruttimann, U., and Unser, M. (1998). A pyramid approach to subpixel registration based on intensity. IEEE Transactions on Image Processing, 7(1), 27–41.CrossRefGoogle ScholarPubMed
Toutin, T. (2003). Error tracking in IKONOS geometric processing. Photogrammetric Engineering & Remote Sensing, 69(1), 43–51.CrossRefGoogle Scholar
Toutin, T. (2004). Review article: Geometric processing of remote sensing images: Models, algorithms and methods. International Journal of Remote Sensing, 25(10), 1893–1924.CrossRefGoogle Scholar
Townshend, J., Justice, C., Gurney, C., and McManus, J. (1992). The impact of misregistration on change detection. IEEE Transactions on Geoscience and Remote Sensing, 30(5), 1054–1060.CrossRefGoogle Scholar
Tsao, J. (2003). Interpolation artifacts in multimodality image registration based on maximization of mutual information. IEEE Transactions on Medical Imaging, 22(7), 854–864.CrossRefGoogle ScholarPubMed
Tuytelaars, T. and Mikolajczyk, K. (2008). Local Invariant Feature Detectors: A Survey. Hanover, MA: Now Publishers, Inc.Google Scholar
Verbyla, D. L. and Boles, S. H. (2000). Bias in land cover change estimates due to misregistration. International Journal of Remote Sensing, 21(18), 3553–3560.CrossRefGoogle Scholar
Viola, P. A. and Wells, W. M. I. (1997). Alignment by maximization of mutual information. International Journal of Computer Vision, 24(2), 137–154.CrossRefGoogle Scholar
Wang, G., Gertner, G. Z., Shoufan, F., and Anderson, A. B. (2005). A methodology for spatial uncertainty analysis of remote sensing and GIS products. Photogrammetric Engineering & Remote Sensing, 71(12), 1423–1432.CrossRefGoogle Scholar
Wang, H. and Ellis, E. C. (2005a). Image misregistration error in change measurements. Photogrammetric Engineering & Remote Sensing, 71(9), 1037–1044.CrossRefGoogle Scholar
Wang, H. and Ellis, E. C. (2005b). Spatial accuracy of orthorectified IKONOS imagery and historical aerial photographs across five sites in China. International Journal of Remote Sensing, 26(9), 1893–1911.CrossRefGoogle Scholar
Wang, J., Di, K., and Li, R. (2005). Evaluation and improvement of geopositioning accuracy of IKONOS stereo imagery. Journal of Surveying Engineering, 131(2), 35–42.CrossRefGoogle Scholar
Wang, W.-H. and Chen, Y.-C. (1997). Image registration by control points pairing using the invariant properties of line segments. Pattern Recognition Letters, 18(3), 269–281.CrossRefGoogle Scholar
Wolfe, R. E., Nishihama, M., Fleiga, A. J., Kuyper, J. A., Roy, D. P., Storey, J. C., and Patt, F. S. (2002). Achieving sub-pixel geolocation accuracy in support of MODIS land science. Remote Sensing of Environment, 83(1–2), 31–49.CrossRefGoogle Scholar
Wong, A. and Clausi, D. A. (2007). ARRSI: Automatic registration of remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing, 45(5), 1483–1493.CrossRefGoogle Scholar
Xia, M. and Liu, B. (2004). Image registration by “super-curves”. IEEE Transactions on Image Processing, 13(5), 720–732.CrossRefGoogle ScholarPubMed
Xiong, X., Che, N., and Barnes, W. (2005). Terra MODIS on-orbit spatial characterization and performance. IEEE Transactions on Geoscience and Remote Sensing, 43(2), 355–365.CrossRefGoogle Scholar
Yam, S. and Davis, L. (1981). Image registration using generalized Hough transform. In Proceedings of the IEEE Conference on Pattern Recognition and Image Processing, Dallas, Texas, pp. 525–533.
Yang, G., Stewart, C. V., Sofka, M., and Tsai, C. L. (2006). Automatic robust image registration system: Initialization, estimation, and decision. In Proceedings of the Fourth IEEE International Conference on Computer Vision Systems, New York, NY, pp. 23–31.
Yang, Z. and Cohen, F. S. (1999). Image registration and object recognition using affine invariants and convex hulls. IEEE Transactions on Image Processing, 8(7), 934–947.CrossRefGoogle ScholarPubMed
Yuan, Z.-M., Wu, F., and Zhuang, Y.-T. (2006). Multi-sensor image registration using multi-resolution shape analysis. Journal of Zhejiang University SCIENCE A, 7(4), 549–555.CrossRefGoogle Scholar
Zagorchev, L. and Goshtasby, A. (2006). A comparative study of transformation functions for nonrigid image registration. IEEE Transactions on Image Processing, 15(3), 529–538.CrossRefGoogle ScholarPubMed
Zavorin, I. and Moigne, J. (2005). Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery. IEEE Transactions on Image Processing, 14(6), 770–782.CrossRefGoogle ScholarPubMed
Zhou, G. and Li, R. (2000). Accuracy evaluation of ground points from IKONOS high-resolution satellite imagery. Photogrammetric Engineering & Remote Sensing, 66(3), 1103–1112.Google Scholar
Zitová, B. and Flusser, J. (2003). Image registration methods: A survey. Image and Vision Computing, 21(11), 977–1000.CrossRefGoogle Scholar
Zokai, S. and Wolberg, G. (2005). Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations. IEEE Transactions on Image Processing, 14(10), 1422–1434.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×