The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A Novel Point-Matching Algorithm Based on Fast Sample Consensus for Image Registration
Abstract
Robustness and accuracy are the two main challenging problems in feature-based remote sensing image registration. In this letter, a novel point-matching algorithm is proposed. An improved random sample consensus (RANSAC) algorithm called fast sample consensus (FSC) is proposed. It divides the data set in RANSAC into two parts: the sample set and the consensus set. Sample set has high correct rate and consensus set has a large number of correct matches. An iterative method is put forward to increase the number of correct correspondences. A set of measures has been used to evaluate the registration result. The performance of the proposed method is validated on the evaluation of these measures and the mosaic images. FSC can get more correct matches than RANSAC in less number of iterations, iterative selection of correct matches algorithm and removal of the imprecise points algorithm effectively increase the accuracy of the result. Extensive experimental studies compared with three state-of-the-art methods prove that the proposed algorithm is robust and accurate.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
A performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723
Cited by
Remote Sensing Image Registration With Modified SIFT and Enhanced Feature Matching
Ma, W., Wen, Z., Wu, Y., Jiao, L., Gong, M., Zheng, Y., & Liu, L. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2600858 · Cited by 389
Self-citationA deep learning framework for remote sensing image registration
Wang, S., Quan, D., Liang, X., Ning, M., Guo, Y., & Jiao, L. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.12.012 · Cited by 292
Self-citationGuided Locality Preserving Feature Matching for Remote Sensing Image Registration
Ma, J., Jiang, J., Zhou, H., Zhao, J., & Guo, X. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2820040 · Cited by 283
Metadata
- Authors
- Wu, Y., Ma, W., Gong, M., Su, L., & Jiao, L.
- Venue
- IEEE Geoscience and Remote Sensing Letters
- Published
- 2015-01
- Volume
- 12
- Issue
- 1
- Pages
- 43-47
- ISSN
- 1545-598X (ISSN-L)
- 1558-0571
- OpenAlex
- W2047243114
- Cited by
- 342
- References
- 3
- FWCI
- 12.17
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wu, Y., Ma, W., Gong, M., Su, L., & Jiao, L. (2015). A Novel Point-Matching Algorithm Based on Fast Sample Consensus for Image Registration. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/lgrs.2014.2325970
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