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Remote Sensing Image Automatic Registration on Multi-scale Harris-Laplacian

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Abstract

In order to overcome the difficulty of automatic image registration in image preprocessing, this paper presents an automatic registration algorithm for remote sensing images with different spatial resolutions. The algorithm is studied based on Harris-Laplacian corner detection, which can determine the affine transformation (zoom, rotation, translation) between images of different scales. The corners in the reference and registration images are firstly detected and located by a multi-scale Harris-Laplacian (H-L) corner detector. Secondly, the algorithm chooses SURF (Speeded Up Robust Feature) descriptor to calculate the detected corners descriptors. Then, the multi-resolution corner matching is achieved based on Euclid distance. Finally, according to the LoG (Laplacian Of Gaussian), the scale factor is automatically determined between reference and registration images. A number of remote sensing images are tested, and the experiments show that the studied algorithm can register two remote sensing images of different sizes and resolutions automatically. It also verifies that the algorithm has the lower time cost comparing with the other existing algorithms (e.g. SIFT) within certain detecting accuracy level. This algorithm is also useful for resolving the problem of potential errors due to parallax effects when establishing geometric affine transformation on corners for detecting on buildings with different unknown elevations.

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References

  • Barbara Z. and Ja F, “Image registration method: a survey”, Image and Vision Computing”, 977–1000. 2003.

  • Chen, H. M., Varshney, P., & Arora, M. (2003). Mutual information based image registration for remote sensing data. International Journal of Remote Sensing, 24(18), 3701–3706.

    Article  Google Scholar 

  • Dae-Ho, L., et al. (2011). Region-based corner detection by radial projection. Journal of the Optical Society of Korea, 15(2), 152–154.

    Article  Google Scholar 

  • Dufournard, Y., Schmid, C., & Horaud, R. (2000). Matching images with different resolutions. Proceedings IEEE Conf. Computer Vision Pattern Recognition, 1, 612–618.

    Google Scholar 

  • Fan, J., Hu, L., & Hu, L. (2010). Improved approach for image registration based on wavelet transform. Computer Engineering, 36, 212–214.

    Google Scholar 

  • Forlenza, L., et al. (2012). Real time corner detection for miniaturized electro-optical sensors onboard small unmanned aerial systems. Sensors, 12(1), 863–877.

    Article  Google Scholar 

  • Gueguen, L., & Pesaresi, M. (2011). Multi scale Harris corner detector based on differential morphological decomposition. Pattern Recognition Letters, 32(14), 1714–1719.

    Article  Google Scholar 

  • Harris C. and Stephens M., “A combined corner and edge detector”, Proceedings of The Fourth Alvey Vision Conference, pp. 147–151 (1998).

  • Herbert, B., Andreas, E., Tinne, T., & Van Luc, G. (2008). SURF: speeded up robust features. Computer Vision and Image Understanding (CVIU), 110, 346–359.

    Article  Google Scholar 

  • Hui Lin, Peijun Du, “Image Registration Based on Corner Detection and Affine Transformation”, International Congress on Image and Signal Processing (CISP2010), 2010.

  • Li, W., & Leung, H. (2004). A maximum likelihood approach for image registration using control point and intensity. Proceedings IEEE transactions on image processing, 13, 1115–1127.

    Article  Google Scholar 

  • Lionel Gueguen, Pierre Soille and Martino Pesaresi, “A new built-up presence index based on density of corners”, IEEE.IGARSS, 2012.

  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60, 91–110.

    Article  Google Scholar 

  • Matthew Brown, Richard Szeliski and Simon Winder, “Multi-Image Matching using Multi-Scale Oriented Patches”, Proceedings of the 2005 I.E. Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005.

  • Mikolajczyk, K., & Schmid, C. (2007). A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 1615–1630.

    Article  Google Scholar 

  • Mikolajczyk K. & Schmid C., “Indexing based on scale invariant interest points”, in Proceedings of the 8th International Conference on Computer Vision, Vancouver, Canada, 525–531, 2001.

  • Paulo Ricardo Possa, Student Member, IEEE, Sidi Ahmed Mahmoudi, Naim Harb, “A Multi-Resolution FPGA-Based Architecture for Real-Time Edge and Corner Detection”, IEEE Transaction on Computers, Vol. 63, No. 10, October 2014.

  • Pei S.C. and Ding J.J, “Improved Harris’ algorithm for corner and edge detections”, Image Processing, ICIP 2. 57–60. 2006.

  • Rosten, E., Porter, R., & Drummond, T. (2010). Faster and better: a machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(1), 105–119.

    Article  Google Scholar 

  • Schmid, C., Mohr, R., & Bauckhage, C. (2000). Evaluation of interest point detectors. International Journal of Computer Vision, 37(2), 151–172.

    Article  Google Scholar 

  • Szeliski, R. (2010). Computer vision: algorithms and applications. New York: Springer.

    Google Scholar 

  • Wang, W. X. (2008). Fragment size estimation without image segmentation. International Journal: Imaging Science Journal, 56, 91–96.

    Google Scholar 

  • Wang, W. X. (2011). Colony image acquisition system and segmentation algorithms. Optical Engineering, 50(12), 123001.

    Article  Google Scholar 

  • Wang, W. X., Luo, D. J., & Li, W. S. (2009). Algorithm for automatic image registration on Harris-Laplace, features. Journal of Applied Remote Sensing, 3, 033554.

    Article  Google Scholar 

  • Ying Ding, Jing-tao Fan and Hua-min Yang, “A Robust Medical Image Registration Algorithm Based on The SAM of Multi-Scale Harris Corners”, IEEE Conf. Biomedical Engineering and Information, BMEI, 2009.

  • Zhang H., Xu, D. “A fast detection algorithm of Harris apparent corners based on the local features”, IEEE. Conf. Intelligent Control and Automation (WCICA), 2011.

  • Zhao X.M, Wang, W.X., and Wang L.P., “Parameter optimal determination for Canny edge detection”, International Journal: Imaging Science Journal, Vol.59, No.6, pp.332-341(10), November 2011.

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Acknowledgments

This research is financially supported by the National Natural Science Fund in China (grant no. 61170147), the Science and Technology Bureau of Shaanxi Province in China with number 2013KW03, and Special Fund for Basic Scientific Research of Central Colleges, Chang’an University in China (grant no. CHD2013G2241019).

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Authors and Affiliations

  1. School of Information Engineering, Chang’An University, Xi’An, 710064, China

    Wang Weixing, Cao Ting & Liu Sheng

  2. Royal Institute of Technology, Stockholm, Sweden

    Wang Weixing

  3. Shanghai Jiaotong University, Shanghai, China

    Tu Enmei

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  1. Wang Weixing
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  2. Cao Ting
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  3. Liu Sheng
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  4. Tu Enmei
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Correspondence to Wang Weixing.

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Weixing, W., Ting, C., Sheng, L. et al. Remote Sensing Image Automatic Registration on Multi-scale Harris-Laplacian. J Indian Soc Remote Sens 43, 501–511 (2015). https://doi.org/10.1007/s12524-014-0432-2

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  • DOI: https://doi.org/10.1007/s12524-014-0432-2

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