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基于小波變換和遺傳算法的遙感影像匹配方法的研究

徐建斌,  洪文,  吳一戎

徐建斌, 洪文, 吳一戎. 基于小波變換和遺傳算法的遙感影像匹配方法的研究[J]. 電子與信息學(xué)報(bào), 2005, 27(2): 283-285.
引用本文: 徐建斌, 洪文, 吳一戎. 基于小波變換和遺傳算法的遙感影像匹配方法的研究[J]. 電子與信息學(xué)報(bào), 2005, 27(2): 283-285.
Xu Jian-bin, Hong Wen, Wu Yi-rong. The Study of Remote Sensing Images Matching Method Based on Wavelet Transform and Genetic Algorithms[J]. Journal of Electronics & Information Technology, 2005, 27(2): 283-285.
Citation: Xu Jian-bin, Hong Wen, Wu Yi-rong. The Study of Remote Sensing Images Matching Method Based on Wavelet Transform and Genetic Algorithms[J]. Journal of Electronics & Information Technology, 2005, 27(2): 283-285.

基于小波變換和遺傳算法的遙感影像匹配方法的研究

The Study of Remote Sensing Images Matching Method Based on Wavelet Transform and Genetic Algorithms

  • 摘要: 該文提出了利用小波分析和遺傳算法實(shí)現(xiàn)遙感影像的匹配,在對(duì)參考影像和目標(biāo)影像進(jìn)行小波分解的基礎(chǔ)上,利用遺傳算法優(yōu)良的全局尋優(yōu)特點(diǎn)先對(duì)低分辨率的遙感影像進(jìn)行匹配操作,然后逐級(jí)上推,最終實(shí)現(xiàn)全分辨率情況下的遙感影像的匹配。實(shí)驗(yàn)結(jié)果表明,該算法不僅具有較高的匹配精度,而且具有較高的效率。
    Abstract: In this paper, a matching method based on wavelet transform and genetic algorithms is proposed. First, the reference images and target images are decomposed to low resolution images using wavelet transform, Then, genetic algorithms, which are useful in finding global optima, are used to match the lowest resolution images. Based on the matching result, the matching between the higher resolution images can be implemented stepwise up to the full resolution images. The results confirm the proposed algorithm can improve the efficiency greatly while guarantee the accuracy.
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  • Brown L G. A survey of image registration techniques[J].ACM Computing Surveys.1992, 24(4):325-[2]Huttenlocher D P, Klanderman G A, Rueklidge W J. Comparing images using the Hausdorff distance. Trans. on PAMI, 1993, 15(9):850 - 863.[3]Thevenaz P.[J].Unser M. A pyramid approach to sub-pixel image fusion based on mutual information, Proc. IEEE Intl. Conf. on Image Processing, ICIP96, Lausanne, Switzerland.1996,:-[4]Sharma R K, Pavel M. Multisensor image registration. Proc.Society for Information Display, 1997, vol. XXVIII: 951 - 954.[5]Starink J P P, Baker. E. Finding point correspondence using simulated annealing, Pattern Recognition ,1995, 28(2):231 - 240.[6]Sawhney H S, Kumar R, True multi-image alignment and its applications to mosaicing and lens distortion correction. IEEE Trans. on PAMI, 1999, 21(3): 235 - 243.[7]Mallat S. A theory for multiresolution signal decompositlon: The wavelet representation[J]. IEEE Trans. on PAMI, 1989, 11(7):674 - 693.[8]Chalermwat P, EI-Ghazawi T, Le Moigne J. Two-phase genetic algorithm-based image registration on parallel clusters[J].Journal of Future Generation Computing Systems.2001, 17:467-[9]Brumby S, Theiler J, Perkins S, et al.. Investigation of feature extraction by a genetic algorithm, 1999, Proc. SPIE, 3812:24- 31.[10]Mitre S K, Murthy C A, Kundu M K. Technique for fractal image compression using genetic algorithm[J].IEEE Trans. on Image Processing.1998, 7(4):586-[11]Mallat S, Zhong Siren. Characterization of signals from multiscale edges[J]. IEEE Trans. on PAMI, 1992, 14(7): 710-732.[12]Holland J H. Adaptation in Natural and Artificial System. Ann Arbor: University of Michigan Press, 1975:30 - 58.[13]Pratt W K. Correlation Techniques of Image Registration[J].IEEE Trans. on Aerospace and Electronics Systems.1974, AES-10(3):353-
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出版歷程
  • 收稿日期:  2003-10-08
  • 修回日期:  2004-01-02
  • 刊出日期:  2005-02-19

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