Feature Matching Improvement through Merging Features for Remote Sensing Imagery
- 34 Downloads
Feature matching is the core stage for object recognition, tracking and several applications of computer vision. Low resolution images have various limitations with respect to spatial, spectral, pixel and temporal information which reduces the performance of image processing approaches. We have combined SURF features with FAST and BRISK features individually in order to provide an optimal solution for feature matching. Furthermore, feature matching has exploited through combined features and compared the performance with state-of-the-art methods. Lastly, RANSAC and MSAC were utilized to eliminate the wrong matches to get optimal feature matches. The experimental results show that the combination of FAST–SURF and BRISK–SURF perform feature matching optimally according to the number of feature matches and estimated time.
KeywordsFeature matching SURF FAST BRISK RANSAC
This work was supported by the National Natural Science Foundation of China under Grants 61471148.
- 4.Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. In 2011 IEEE international conference on computer vision (ICCV) (pp. 2564–2571). IEEE.Google Scholar
- 5.Leutenegger, S., Chli, M., & Siegwart, R. Y. (2011). BRISK: Binary robust invariant scalable keypoints. In 2011 IEEE international conference on computer vision (ICCV) (pp. 2548–2555). IEEE.Google Scholar
- 7.Harris, C. G., & Stephens, M. J. (1988). A combined corner and edge detector. In Alvey vision conference, Manchester, UK (pp. 147–151).Google Scholar
- 8.Azad, P., Asfour, T., & Dillmann, R. (2009). Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition. In IEEE/RSJ international conference on intelligent robots and systems, 2009. IROS 2009 (pp. 4275–4280). IEEE.Google Scholar
- 11.Karami, E., Prasad, S., & Shehata, M. (2017). Image matching using SIFT, SURF, BRIEF and ORB: Performance comparison for distorted images. arXiv preprint arXiv:1710.02726.
- 12.Ren, S., & Li, J. (2018). An improved matching method base on SURF. In Tenth international conference on digital image processing (ICDIP 2018) (Vol. 10806, p. 108062D). International Society for Optics and Photonics.Google Scholar
- 13.Guerrero, M. (2011). A comparative study of three image matching algorithms: Sift, surf, and fast. All Graduate Theses and Dissertations. Paper 1040. https://digitalcommons.usu.edu/etd/1040. Accessed 12 Sept 2018.
- 15.Huijuan, Z., & Qiong, H. (2011). Fast image matching based-on improved SURF algorithm. In 2011 International conference on electronics, communications and control (ICECC) (pp. 1460–1463). IEEE.Google Scholar
- 17.Pan, J., Chen, W., & Peng, W. (2013). A new moving objects detection method based on improved SURF algorithm. In Control and decision conference (CCDC), 2013 25th Chinese (pp. 901–906). IEEE.Google Scholar
- 22.Gu, L. (2007). An automatic target image mosaic algorithm. In The international conference on EUROCON, 2007. Computer as a tool (pp. 369–374). IEEE.Google Scholar
- 23.Wei, W., Jun, H., & Yiping, T. (2008). Image matching for geomorphic measurement based on SIFT and RANSAC methods. In 2008 international conference on computer science and software engineering (pp. 317–320). IEEE.Google Scholar