GPU-accelerated large-size VHR images registration via coarse-to-fine matching

Computers and Geosciences

Volumn 66, Issue , 2014, Pages 54-65

  Zhang, Yunsheng ^a   Zhou, Peilong ^a   Ren, Yue ^a   Zou, Zhengrong ^a  

^a CENTRAL SOUTH UNIVERSITY   (China)

Author keywords

Coarse to fine; Compute unified device architecture; Image registration; Remote sensing image

Indexed keywords

EID: 84894487185     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2014.01.011     Document Type: Article

References (48)

1. Arévalo V., González J. Improving piecewise linear registration of high-resolution satellite images through mesh optimization. IEEE Trans. Geosci. Remote Sens. 2008, 46(11):3792-3803.
2. Bay H., Ess A., Tuytelaars T., Van Gool L. Speeded-up robust features (SURF). Comput. Vision Image Understanding 2008, 110(3):346-359.
3. Bentoutou Y., Taleb N., Kpalma K., Ronsin J. An automatic image registration for applications in remote sensing. IEEE Trans. Geosci. Remote Sens. 2005, 43(9):2127-2137.
4. Bernabé S., López Sebastián, Plaza A., Sarmiento R. GPU Implementation of an automatic target detection and classification algorithm for hyperspectral image analysis. IEEE Geosci. Remote Sens. Lett. 2013, 10(2):221-225.
5. Biswal P., Mondal P., Banerjee S. Parallel architecture for accelerating affine transform in high-speed imaging systems. J. Real-Time Image Process. 2013, 8(1):69-79.
6. Cheng L., Gong J., Yang X., Fan C., Han P. Robust affine invariant feature extraction for image matching. IEEE Geosci. Remote Sens. Lett. 2008, 5(2):246-250.
7. CUDA SURF, (accessed 01.04.13). http://www.d2.mpi-inf.mpg.de/surf.
8. Deshmukh M., Bhosle U. A survey of image registration. Int. J. Image Process. 2011, 5(3):245-269.
9. Fischler M., Bolles R. Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Commun. ACM 1981, 24(6):381-395.
10. González C., Resano J., Plaza A., Mozos D. FPGA implementation of abundance estimation for spectral unmixing of hyperspectral data using the image space reconstruction algorithm. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5(1):248-261.
11. Harris, C., Stephens, M., 1988. A combined corner and edge detector. In: Proceedings of 4th Alvey Vision Conference, Manchester, pp. 147-151.
12. Huo C., Pan C., Huo L., Zhou Z. Multilevel SIFT matching for large-size VHR image registration. IEEE Geosci. Remote Sens. Lett. 2012, 9(2):171-175.
13. Hong G., Zhang Y. Wavelet-based image registration technique for high-resolution remote sensing images. Comput. Geosci. 2008, 34(12):1708-1720.
14. Jeong I., Hong M., Hahn K., Choi J., Kim C. Performance study of satellite image processing on graphics processors unit using CUDA. Korean J. Remote Sens. 2012, 28(6):683-691.
15. Kim T., Im Y. Automatic satellite image registration by combination of matching and random sample consensus. IEEE Trans. Geosci. Remote Sens. 2003, 41(5):1111-1117.
16. Lee C., Gasster S., Plaza A., Chang C., Huang B. Recent developments in high performance computing for remote sensing: a review. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2011, 4(3):508-527.
17. Li D. Remote sensing in the Wenchuan earthquake. Photogramm. Eng. Remote Sens. 2009, 75(5):506-509.
18. 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(2):287-291.
19. Liu Z., An J., Jing Y. A simple and robust feature point matching algorithm based on restricted spatial order constraints for aerial image registration. IEEE Trans. Geosci. Remote Sens. 2012, 50(2):514-527.
20. Liu, H., Yang, J., Liu, H., Zhang, J., 2010. A new parallel ortho-rectification algorithm in a cluster environment. In: Proceedings of the 3rd International Congress on Image and Signal Processing, Yantai 5, 2080-2084.
21. Lowe.D. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 2004, 60(2):91-110.
22. Matas, J., Chum, O., Urban, M., Pajdla, T., 2002. Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of the British Machine Vision Conference, pp. 384-396.
23. Mikolajczyk K., Tuytelaars T., Schmid C., Zisserman A., Matas J., Schaffalitzky F., Kadir T., Van Gool L. A comparison of affine region detectors. Int. J. Comput. Vision 2005, 65(1-2):43-72.
24. NVIDIA CUDA, (accessed 01.04.13). http://www.nvidia.com/object/cuda_home_new.html.
25. Reguera-Salgado J., Calvino-Cancela M., Martín-Herrero J. GPU geocorrection for airborne pushbroom imagers. IEEE Trans. Geosci. Remote Sens. 2012, 50(11):4409-4419.
26. Rublee, E., Rabaud, V., Konolige, K., Bradski, G., 2011. ORB: An efficient alternative to SIFT or SURF. In: Proceedings of the 2011 IEEE International Conference on Computer Vision, Barcelona, Spain, pp. 2564-2571.
27. Rosten E., Porter R., Drummond T. Faster and better: a machine learning approach to corner detection. IEEE. Trans. Pattern Anal. Mach. Intell. 2010, 32(1):105-119.
28. Sedaghat A., Mokhtarzade M., Ebadi H. Uniform robust scale-invariant feature matching for optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 2011, 49(11):4516-4527.
29. SiftGPU, (accessed 01.04.13). http://cs.unc.edu/~ccwu/siftgpu/.
30. Thomas U., Kurz F., Rosenbaum D., Mueller R., Reinartz P. GPU-based orthorectification of digital airborne camera images in real time. Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci., Beijing, China 2008, 89-594.
31. Thomas U., Rosenbaum D., Kurz F., Suri S., Reinartz P. A new software/hardware architecture for real time image processing of wide area airborne camera images. J. Real-Time Image Process. 2009, 4(3):229-244.
32. Tuytelaars T., Mikolajczyk K. Local invariant feature detectors: a survey. Found. Trends Comput. Gr. Vision 2008, 3(3):177-280.
33. Wen G., Lv J., Yu W. A high-performance feature-matching method for image registration by combining spatial and similarity information. IEEE Trans. Geosci. Remote Sens. 2008, 46(4):1266-1277.
34. Wong A., Clausi D. ARRSI: automatic registration of remote-sensing images. IEEE Trans. Geosci. Remote Sens. 2007, 45(5):1483-1493.
35. Wu B., Zhang Y., Zhu Q. A triangulation-based hierarchical image matching method for wide-baseline images. Photogramm. Eng. Remote Sens. 2011, 77(7):695-708.
36. Wu Z., Goshtasby A. Adaptive image registration via hierarchical Voronoi subdivision. IEEE Trans. Image Process. 2012, 21(5):2464-2473.
37. Xiong Z., Zhang Y. A novel interest-point-matching algorithm for high-resolution satellite images. IEEE Trans. Geosci. Remote Sens. 2009, 47(12):4189-4200.
38. Xiong Z., Zhang Y. A critical review of image registration methods. Int. J. Image Data Fus. 2010, 1(2):137-158.
39. Yu L., Zhang D., Holdenet E-J. A fast and fully automatic registration approach based on point features for multi-source remote-sensing images. Comput. Geosci. 2008, 34(7):838-848.
40. Yoo, S., Lee, T., Choi, K., Jeong, C., 2008a. Fast image registration for multisensor fusion using graphics hardware. In: Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Seoul, Korea, pp. 321-325.
41. Yoo S., Lee Y., Jo S., Jeong C. GPU implementation of a clustering based image registration. LNCS 2008, 5226:491-497.
42. Zagorchev L., Goshtasby A. A comparative study of transformation functions for nonrigid image registration. IEEE Trans. Image Process. 2006, 15(3):529-538.
43. Zanin, M., Remondino, F., Mura, M., 2012. High-performance computing in image registration. In: Proceedings of SPIE 8539, High-Performance Computing in Remote Sensing II.
44. Zitová B., Flusser J. Image registration methods: a survey. Image Vision Comput. 2003, 21(11):977-1000.
45. Zhang C., Fraser C. Automated registration of high-resolution satellite images. Photogramm. Rec. 2007, 22(117):75-87.
46. Zhang Z., Zhang Y., Ke T., Guo D. Photogrammetry for first response in Wenchuan earthquake. Photogramm. Eng. Remote Sens. 2009, 75(5):510-513.
47. Zhang Z., Zhang J., Liao M., Zhang L. Automatic registration of multisource imagery based on global image matching. Photogramm. Eng. Remote Sens. 2000, 66(5):625-629.
48. Zhu Q., Wu B., Wan N. A filtering strategy for interest point detecting to improve repeatability and information content. Photogramm. Eng. Remote Sens. 2007, 73(5):547-553.