Abstract
Aiming at the splicing problem of Synthetic Aperture Radar (SAR) image, an improved algorithm for SURF is proposed to realize the fast splicing of SAR image. The SURF feature descriptor has scale invariance and rotation invariance, and has strong robustness to light intensity and affine transmission variation. The improved algorithm uses machine learning methods to build a binary classifier that identifies the key feature points in the SURF extracted feature points and eliminates the key feature points. In addition, the relief-F algorithm is used to reduce the dimensionality of the improved SURF descriptor to complete image registration. In the image fusion stage, a weighted fusion algorithm with a threshold is used to achieve seamless image mosaic. Experimental results show that the improved algorithm has strong real-time performance and robustness, and improves the efficiency of image registration. It can accurately mosaic multiple SAR images.
Foundation Items: The National Natural Science Fundation of China (61531020, 61471383).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bai, Z., He, J., Yuan, Q.: Improving image stitching accuracy for double CCD. J. Appl. Opt. 31(6), 918–921 (2010)
Liu, H., Motoda, H.: Computational Methods of Feature Selection, pp. 277–290. Chapman & Hall/CRC, Boca Raton (2007)
Zhang, Y.: Research on Image and Video Stitching Technology Based on SURF Features. Xi’an University of Science and Technology, Xi’an (2013)
Bay, H., Ess, A., Tuytelaars, T., et al.: SURF: speeded up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the 7th IEEE International Conference on Computer Vision, pp. 1150–1157. IEEE Press, Piscataway (1999)
Sergieh, H.M., Egyed-Zsigmond, E., Doller, M., et al.: Improving SURF image matching using supervised learning. In: Proceedings of the 2012 8th International Conference on Signal Image Technology and Internet Based Systems, pp. 230–237. IEEE Press, Piscataway (2012)
Provost, F.: Machine learning from imbalanced data sets 101. In: Proceedings of the AAAI 2000 Workshop on Imbalanced Data Sets, pp. 359–367. AAAI Press, Palo Alto (2000)
Zhou, D., He, M., Yang, Q.: A robust seamless image stitching algorithm based on feature points. Meas. Control. Technol. 28(6), 32–36 (2009)
Guo, J.: A study on image-based cylinder panoramic image generation technology. Xi’an University of Science and Technology, Xi’an (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Yao, H., Wang, H., Lin, X. (2018). A SAR Image Fast Stitching Algorithm Based on Machine Learning. In: Meng, L., Zhang, Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-00557-3_55
Download citation
DOI: https://doi.org/10.1007/978-3-030-00557-3_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00556-6
Online ISBN: 978-3-030-00557-3
eBook Packages: Computer ScienceComputer Science (R0)