Volume 10 Issue 3
May  2017
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QI Bing-jie, LIU Jin-guo, ZHANG Bo-yan, ZUO Yang, LYU Shi-liang. Research on matching performance of SIFT and SURF algorithms for high resolution remote sensing image[J]. Chinese Optics, 2017, 10(3): 331-339. doi: 10.3788/CO.20171003.0331
Citation: QI Bing-jie, LIU Jin-guo, ZHANG Bo-yan, ZUO Yang, LYU Shi-liang. Research on matching performance of SIFT and SURF algorithms for high resolution remote sensing image[J]. Chinese Optics, 2017, 10(3): 331-339. doi: 10.3788/CO.20171003.0331

Research on matching performance of SIFT and SURF algorithms for high resolution remote sensing image

Funds:

National Natural Science Foundation of China 61405191

  • Received Date: 18 Jan 2017
  • Rev Recd Date: 27 Mar 2017
  • Publish Date: 01 Jun 2017
  • Image matching is the basis of image rectification and mosaic. Because of higher features similarity and smaller overlapped area than ordinary images, the remote sensing images have higher requirements on matching algorithm in both performance and iteration speed. The performances in three aspects:feature detection, feature description and feature matching, are analyzed between the SIFT algorithm and the SURF algorithm in terms of speed and accuracy. The requirements of the degree of overlapping between remote sensing images and the matching distance of the genvector is discussed as well. In view of the characteristic that SURF algorithm is sensitive to the error in feature detection, oSURF algorithm is presented in this paper. Finally, the advantages and disadvantages of each algorithm are analyzed by using satellite remote sensing data of level 1A. The results show that iteration speed of SURF algorithm is three times faster than SIFT algorithm. Under the same matching rate, the width of overlapped area on image required in SURF algorithm is 1.25 times of the dimension of genvector but 1.5 times in SIFT, and the accuracy of oSURF algorithm is increased by 5%~10% compared with SURF algorithm in the same computation speed, which indicate that oSURF is more suitable for remote sensing image stitching.

     

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