A New Fast Matching Algorithm by Trans-Scale Search for Remote Sensing Image

QI Youjie; ZHU En

doi:10.1049/cje.2015.07.037

Volume 24 Issue 3

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2015 > 24(3): 654-660

QI Youjie and ZHU En, “A New Fast Matching Algorithm by Trans-Scale Search for Remote Sensing Image,” Chinese Journal of Electronics, vol. 24, no. 3, pp. 654-660, 2015, doi: 10.1049/cje.2015.07.037

Citation:

QI Youjie and ZHU En, “A New Fast Matching Algorithm by Trans-Scale Search for Remote Sensing Image,” Chinese Journal of Electronics, vol. 24, no. 3, pp. 654-660, 2015, doi: 10.1049/cje.2015.07.037

Citation:

PDF( 807 KB)

A New Fast Matching Algorithm by Trans-Scale Search for Remote Sensing Image

doi: 10.1049/cje.2015.07.037

QI Youjie,
ZHU En

College of Information Science and Engineering, Southeast University, Nanjing 210096, China

Received Date: 2014-06-19
Rev Recd Date: 2014-09-05
Publish Date: 2015-07-10

Abstract

Abstract

A new fast matching algorithm for remote sensing images is proposed. The algorithm adopts a coarse to fine matching process. A remote sensing image is decomposed into multi-scale images which form a wavelet image pyramid by the way of DWT (Discrete wavelet transform). Extract a low frequency sub-image from the wavelet image pyramid to carry out a rough matching operation between the sub-image and target image, and then get a rough position after cluster analysis. Get a suitable position with center at the rough position from remote sensing image to accomplish fine matching operation. The algorithm has been analyzed theoretically from the signal processing point of view, and a sufficient condition is given to select wavelet filter. Simulation results testify that proposed algorithm not only distinguishes remote sensing images precisely, but also cuts down matching time greatly. It is only 25.04% of SIFT algorithm, and 35.36% of SURF algorithm for matching time.
- Image matching,
- SIFT,
- DWT,
- Remote sensing

FullText(HTML)

References(17)

References

Y. Zhang, Y. Ji, L. Ma, et al., "A recognition and change detection method for buildings in remote sensing images", Acta Electronica Sinica, Vol.42, No.4, pp.653-657, 2014. (in Chinese)

Y. An and G. He, "A new algorithm for object-oriented multiscale high resolution remote sensing image segmentation", IEEE 4^th International Congress on Image and Signal Processing, Shanghai, China, Vol.3, pp.1596-1599, 2011.

D.G. Lowe, "Object recognition from local scale-invariant features", IEEE Proceedings of the International Conference on Computer Vision, Kerkyra, Greece, Vol.2, pp.1150-1157, 1999.

D.G. Lowe, "Distinctive image features from scale-invariant keypoints", The International Journal of Computer Vision, Vol.60, No.2, pp.91-110, 2004.

J. Koenderink, "The structure of images", Biological Cybernetics, Vol.50, No.5, pp.363-396, 1984.

T. Lindeberg, "Scale-space theory: A basic tool for analysing structures at different scales", Journal of Applied Statistics, Vol.21, No.2, pp.224-270. 1994.

S.G. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation", IEEE Trans on PAMI, Vol.11, No.7, pp.674-693, 1989.

R. Jiao, Y. Li and J. Hou, "Remote sensing image compression based on vision model and image feature", Journal of Beijing University of Aeronautics and Astronautics, Vol.31, No.2, pp.197-201, 2005.

C. Carlson, "Thresholds for perceived image sharpness", Photog. Sci. Eng., Vol.22, No.2, pp.69-71, 1978.

G. Burton and I. Moorhead, "Color and spatial structure in natural scenes", Appl Opt, Vol.26, No.1, pp.157-170, 1987.

D. Field, "Relations between the statistics of natural images and the response properties of cortical cells", Optical Society of America, Vol.4, No.12, pp.2379-2394, 1987.

D.J. Tolhurst, Y. Tadmor and C. Tang, "Amplitude spectra of natural images", Ophthalmic and Physiological Optics, Vol.12, No.2, pp.229-232, 1992.

L. Yao, F. Hao, Y. Zhu, et al., "An architecture of optimised SIFT feature detection for an FPGA implementation of an image matcher", IEEE International Conference on Field- Programmable Technology, Sydney, NSW, Australia, pp.30-37, 2009.

S. Stephen, N. Ho-kong, Jasiobedzki Piotr, et al., "Vision based modeling and localization for planetary exploration rovers", International Astronautical Federation-55th International Astronautical Congress 2004, Vancouver, Canada, Vol.12, pp.7813- 7823, 2004.

V. Bonato, E. Marques and G. Constantinides, "A parallel hardware architecture for scale and rotation invariant feature detection", IEEE Transactions on Circuits and Systems for Video Technology, Vol.18, No.12, pp.1703-1712, 2008.

S. Zhong, J. Wang, L. Yan, et al., "A real-time embedded architecture for SIFT", Journal of Systems Architecture, Vol.59, No.1, pp.16-29. 2013.

P. Mishra, A. Nidhi, and J. Kishore, "Embedded hardware architectures for scale and rotation invariant feature detection", IEEE International Conference on Electronics, Computing and Communication Technologies, Bangalore, India, pp.1-6, 2014.

Relative Articles

Supplements(0)

Cited By

Proportional views