The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Remote Sensing Image Registration With Modified SIFT and Enhanced Feature Matching
Abstract
The scale-invariant feature transform algorithm and its many variants are widely used in feature-based remote sensing image registration. However, it may be difficult to find enough correct correspondences for remote image pairs in some cases that exhibit a significant difference in intensity mapping. In this letter, a new gradient definition is introduced to overcome the difference of image intensity between the remote image pairs. Then, an enhanced feature matching method by combining the position, scale, and orientation of each keypoint is introduced to increase the number of correct correspondences. The proposed algorithm is tested on multispectral and multisensor remote sensing images. The experimental results show that the proposed method improves the matching performance compared with several state-of-the-art methods in terms of the number of correct correspondences and aligning accuracy.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955 · cited 7×
“Considering the results in Table I, SIFT, SAR-SIFT, and SURF fail to register the test pair P-A.”
MethodInfluentialSpeeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557
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
BRISK: Binary Robust invariant scalable keypoints
Leutenegger, S., Chli, M., & Siegwart, R. Y. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126542 · Cited by 3,450
SAR-SIFT: A SIFT-Like Algorithm for SAR Images
Dellinger, F., Delon, J., Gousseau, Y., Michel, J., & Tupin, F. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2323552 · Cited by 634
A Novel Point-Matching Algorithm Based on Fast Sample Consensus for Image Registration
Wu, Y., Ma, W., Gong, M., Su, L., & Jiao, L. (2015) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2014.2325970 · Cited by 342
Self-citationUniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images
Sedaghat, A., Mokhtarzade, M., & Ebadi, H. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2144607 · Cited by 334
A Novel Coarse-to-Fine Scheme for Automatic Image Registration Based on SIFT and Mutual Information
Gong, M., Zhao, S., Jiao, L., Tian, D., & Wang, S. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2281391 · Cited by 301
Self-citation
Cited by
Image Matching from Handcrafted to Deep Features: A Survey
Ma, J., Jiang, X., Fan, A., Jiang, J., & Yan, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01359-2 · Cited by 990
A 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-citationA Deep Learning Semantic Template Matching Framework for Remote Sensing Image Registration
Li, L., Han, L., Ding, M., Cao, H., & Hu, H. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.09.012 · Cited by 67
Metadata
- Authors
- Ma, W., Wen, Z., Wu, Y., Jiao, L., Gong, M., Zheng, Y., & Liu, L.
- Venue
- IEEE Geoscience and Remote Sensing Letters
- Published
- 2017-01
- Volume
- 14
- Issue
- 1
- Pages
- 3-7
- ISSN
- 1545-598X (ISSN-L)
- 1558-0571
- OpenAlex
- W2559871160
- Cited by
- 389
- References
- 16
- FWCI
- 13.92
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Remote Sensing Image Registration With Modified SIFT and Enhanced Feature Matching. https://app.cochat.ai/papers/10.1109/lgrs.2016.2600858
- The paper
- Ma, W., Wen, Z., Wu, Y., Jiao, L., Gong, M., Zheng, Y., & Liu, L. (2017). Remote Sensing Image Registration With Modified SIFT and Enhanced Feature Matching. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/lgrs.2016.2600858
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
