Automatic image registration (AIR) has been widely studied in the fields of medical imaging, computer vision, and remote sensing. In various cases, such as image fusion, high registration accuracy should be achieved to meet application requirements. For satellite images, the large image size and unstable positioning accuracy resulting from the limited manufacturing technology of charge-coupled device, focal plane distortion, and unrecorded spacecraft jitter lead to difficulty in obtaining agreeable corresponding points for registration using only area-based matching or feature-based matching. In this situation, a coarse-to-fine matching strategy integrating two types of algorithms is proven feasible and effective. In this paper, an AIR method for application to the fusion of ZY-1-02C satellite imagery is proposed. First, the images are geometrically corrected. Coarse matching, based on scale invariant feature transform, is performed for the subsampled corrected images, and a rough global estimation is made with the matching results. Harris feature points are then extracted, and the coordinates of the corresponding points are calculated according to the global estimation results. Precise matching is conducted, based on normalized cross correlation and least squares matching. As complex image distortion cannot be precisely estimated, a local estimation using the structure of triangulated irregular network is applied to eliminate the false matches. Finally, image resampling is conducted, based on local affine transformation, to achieve high-precision registration. Experiments with ZY-1-02C datasets demonstrate that the accuracy of the proposed method meets the requirements of fusion application, and its efficiency is also suitable for the commercial operation of the automatic satellite data process system.
References
[1]
Brook, A.; Ben-Dor, E. Automatic registration of airborne and spaceborne images by topology map matching with SURF processor algorithm. Remote Sens 2011, 3, 65–82.
[2]
Wang, P.; Qu, Z.; Wang, P.; Ying, H.; Zhen, K. A coarse-to-fine matching algorithm for FLIR and optical satellite image registration. IEEE Geosci. Remote Sens. Lett 2012, 9, 599–603.
Bunting, P.; Labrosse, F.; Lucas, R. A multi-resolution area-based technique for automatic multi-modal image registration. Image Vis. Comput 2010, 28, 1203–1219.
[5]
Hasan, M.; Pickering, M.R.; Jia, X. Modified SIFT for Multi-Modal Remote Sensing Image Registration. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22–27 July 2012.
[6]
Song, Z.L.; Zhang, J. Remote sensing image registration based on retrofitted SURF algorithm and trajectories generated from lissajous figures. IEEE Geosci. Remote Sens. Lett 2010, 7, 491–495.
[7]
Al-Ruzouq, R.I. Data fusion of multi-source imagery based on linear features registration. Int. J. Remote Sens 2010, 31, 5011–5021.
[8]
Yu, L.; Zhang, D.; Holden, E.J. A fast and fully automatic registration approach based on point features for multi-source remote-sensing images. Comput. Geosci 2008, 34, 838–848.
[9]
Ma, J.; Chan, J.C.W.; Canters, F. Fully automatic subpixel image registration of multiangle CHRIS/proba data. IEEE Trans. Geosci. Remote Sens 2010, 48, 2829–2839.
[10]
Hu, Z.; Gong, W.; Cai, Z. Multi-resolution remote sensing image registration using differential evolution with adaptive strategy selection. Opt. Eng 2012, doi:10.1117/1.OE.51.10.101707.
Suri, S.; Reinartz, P. Mutual-information-based registration of terraSAR-X and ikonos imagery in urban areas. IEEE Trans. Geosci. Remote Sens 2010, 48, 939–949.
[13]
Wong, A.; Clausi, D.A. ARRSI: Automatic registration of remote-sensing images. IEEE Trans. Geosci. Remote Sens 2007, 45, 1483–1493.
[14]
Lemoine, G.; Giovalli, M. Geo-correction of high-resolution imagery using fast template matching on a GPU in emergency mapping contexts. Remote Sens 2013, 5, 4488–4502.
[15]
Hong, G.; Zhang, Y. Wavelet-based image registration technique for high-resolution remote sensing images. Comput. Geosci 2008, 34, 1708–1720.
[16]
Lee, S.R. A coarse-to-fine approach for remote-sensing image registration based on a local method. Int. J. Smart Sens. Intell. Syst 2010, 3, 690–702.
[17]
Lowe, D.G. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis 2004, 60, 91–110.
[18]
Bay, H.; Ess, A.; Tuytelaars, T.; van Gool, L. Speeded-up robust features (SURF). Comput. Vis. Image Underst 2008, 110, 346–359.
[19]
Cao, S.; Jiang, J.; Zhang, G.; Yuan, Y. An edge-based scale- and affine-invariant algorithm for remote sensing image registration. Int. J. Remote Sens 2013, 34, 2301–2326.
[20]
Yang, Y.; Gao, X. Remote sensing image registration via active contour model. AEU-Int. J. Electron. Commun 2009, 63, 227–234.
[21]
Huang, L.; Li, Z. Feature-based image registration using the shape context. Int. J. Remote Sens 2010, 31, 2169–2177.
[22]
Mikolajczyk, K.; Schmid, C. A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell 2005, 27, 1615–1630.
[23]
Zheng, Y.; Cao, Z.; Xiao, Y. Multi-spectral remote image registration based on SIFT. Electron. Lett 2008, 44, 107–108.
[24]
Fan, B.; Huo, C.; Pan, C.; Kong, Q. Registration of optical and SAR satellite images by exploring the spatial relationship of the improved SIFT. IEEE Geosci. Remote Sens. Lett 2013, 10, 657–661.
[25]
Li, Q.; Wang, G.; Liu, J.; Chen, S. Robust scale-invariant feature matching for remote sensing image registration. IEEE Geosci. Remote Sens. Lett 2009, 6, 287–291.
[26]
Sima, A.A.; Buckley, S.J. Optimizing SIFT for matching of short wave infrared and visible wavelength images. Remote Sens 2013, 5, 2037–2056.
[27]
Goncalves, H.; Corte-Real, L.; Goncalves, J.A. Automatic image registration through image segmentation and SIFT. IEEE Trans. Geosci. Remote Sens 2011, 49, 2589–2600.
[28]
Wang, L.; Niu, Z.; Wu, C.; Xie, R.; Huang, H. A robust multisource image automatic registration system based on the SIFT descriptor. Int. J. Remote Sens 2012, 33, 3850–3869.
[29]
Wu, Z.; Goshtasby, A. Adaptive image registration via hierarchical voronoi subdivision. IEEE Trans. Image Process 2012, 21, 2464–2473.
[30]
Li, Y.; Davis, C.H. A Combined Global and Local Approach for Automated Registration of High-Resolution Satellite Images Using Optimum Extrema Points. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA, 7–11 July 2008.
[31]
Arévalo, V.; González, J. An experimental evaluation of non-rigid registration techniques on quickbird satellite imagery. Int. J. Remote Sens 2008, 29, 513–527.
[32]
Fischler, M.A.; Bolles, R.C. Random Sample Consensus: A Paradigm for Model Fitting with Application to Image Analysis and Automated Cartography. Proceedings of the Communications of the ACM, New York, NY, USA, June 1981.
[33]
Torr, P.H.S.; Zisserman, A. MLESAC: A new robust estimator with application to estimating image geometry. Comput. Vis. Image Understand 2000, 78, 138–156.
Gonzalez, R.C.; Woods, R.E. Digital Image Processing, 2nd ed ed.; Prentice Hall Press: Upper Saddle River, NJ, USA, 2002.
[36]
Ayoub, F.; Leprince, S.; Binet, R.; Lewis, K.W.; Aharonson, O. Influence of Camera Distortions on Satellite Image Registration and Change Detection Applications. Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Boston, MA, USA, 7–11 July 1981.
[37]
Farr, T.G.; Rosen, P.A.; Caro, E.; Crippen, R.; Duren, R.; Hensley, S.; Kobrick, M.; Paller, M.; Rodriguez, E.; Roth, L.; et al. The shuttle radar topography mission. Rev. Geophys 2007, 45, doi:10.1029/2005RG000183.
[38]
Tao, C.V.; Hu, Y. A comprehensive study of the rational function model for photogrammetric processing. Photogramm. Eng. Remote Sens 2001, 67, 1347–1358.
[39]
Hotelling, H. Analysis of a complex of statistical variables into principal components. J. Educ. Psychol 1933, 24, 417–441.
[40]
Harris, C.; Stephens, M. A Combined Corner and Edge Detector. Proceedings of the 4th Alvey Vision Conference, Alvey, UK, 31 August–2 September 1988.
[41]
F？rstner, W.; Gülch, E. A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Centres of Circular Features. Proceedings of the ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, Switzerland, 2–4 June 1987.
[42]
Zhang, Y.; Xiong, J.; Hao, L. Photogrammetric processing of low-altitude images acquired by unpiloted aerial vehicles. Photogramm. Rec 2011, 26, 190–211.
[43]
Ackermann, F. Digital image correlation: Performance and potential application in photogrammetry. Photogramm. Rec 1984, 11, 429–439.
[44]
Zhang, Y.; Wang, B.; Duan, Y. An algorithm of gross error elimination in image matching for large rotation angle images. Geomat. Inf. Sci. Wuhan Univ 2013, 38, 1135–1138.
[45]
Kang, Z.; Zlatanova, S. New Point Matching Algorithm for Panoramic Reflectance Images. Proceedings of the International Symposium on Multispectral Image Processing and Pattern Recognition, Wuhan, China, 15 November 2007.
[46]
Kang, Z.; Li, J.; Zhang, L.; Zhao, Q.; Zlatanova, S. Automatic registration of terrestrial laser scanning point clouds using panoramic reflectance images. Sensors 2009, 9, 2621–2646.
[47]
Lee, D.T.; Schachter, B.J. Two algorithms for constructing a delaunay triangulation. Int. J. Comput. Inform. Sci 1980, 9, 219–242.
[48]
Chavezp, P.; Sides, S.C.; Anderson, J.A. Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic. Photogramm. Eng. Remote Sens 1991, 57, 295–303.
[49]
Gon？alves, H.; Gon？alves, J.A.; Corte-Real, L. Measures for an objective evaluation of the geometric correction process quality. IEEE Geosci. Remote Sens. Lett 2009, 6, 292–296.
