The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images
Abstract
Extracting well-distributed, reliable, and precisely aligned point pairs for accurate image registration is a difficult task, particularly for multisource remote sensing images that have significant illumination, rotation, and scene differences. The scale-invariant feature transform (SIFT) approach, as a well-known feature-based image matching algorithm, has been successfully applied in a number of automatic registration of remote sensing images. Regardless of its distinctiveness and robustness, the SIFT algorithm suffers from some problems in the quality, quantity, and distribution of extracted features particularly in multisource remote sensing imageries. In this paper, an improved SIFT algorithm is introduced that is fully automated and applicable to various kinds of optical remote sensing images, even with those that are five times the difference in scale. The main key of the proposed approach is a selection strategy of SIFT features in the full distribution of location and scale where the feature qualities are quarantined based on the stability and distinctiveness constraints. Then, the extracted features are introduced to an initial cross-matching process followed by a consistency check in the projective transformation model. Comprehensive evaluation of efficiency, distribution quality, and positional accuracy of the extracted point pairs proves the capabilities of the proposed matching algorithm on a variety of optical remote sensing images.
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
A Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
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
Image registration methods: a survey
Zitová, B., & Flusser, J. (2003) · Image and Vision Computing · doi:10.1016/s0262-8856(03)00137-9 · Cited by 6,423
Shape matching and object recognition using shape contexts
Belongie, S., Malik, J., & Puzicha, J. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.993558 · Cited by 6,306
A survey of image registration techniques
Brown, L. G. (1992) · ACM Computing Surveys · doi:10.1145/146370.146374 · Cited by 4,057
PCA-SIFT: a more distinctive representation for local image descriptors
Ke, Y., & Sukthankar, R. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315206 · Cited by 3,237
A comparison of affine region detectors
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., & Gool, L. V. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-3848-x · Cited by 2,939
Evaluation of Interest Point Detectors
Schmid, C., Mohr, R., & Bauckhage, C. (2000) · International Journal of Computer Vision · doi:10.1023/a:1008199403446 · Cited by 1,484
Cited by
PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval
Zhou, W., Newsam, S., Li, C., & Shao, Z. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.01.004 · Cited by 571
A review of multimodal image matching: Methods and applications
Jiang, X., Ma, J., Xiao, G., Shao, Z., & Guo, X. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.012 · Cited by 532
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
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
GPU-accelerated large-size VHR images registration via coarse-to-fine matching
Zhang, Y., Zhou, P., Ren, Y., & Zou, Z. (2014) · Computers & Geosciences · doi:10.1016/j.cageo.2014.01.011 · Cited by 15
Metadata
- Authors
- Sedaghat, A., Mokhtarzade, M., & Ebadi, H.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2011-11
- Volume
- 49
- Issue
- 11
- Pages
- 4516-4527
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2115072079
- Cited by
- 334
- References
- 39
- FWCI
- 8.97
- 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: Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images. https://app.cochat.ai/papers/10.1109/tgrs.2011.2144607
- The paper
- Sedaghat, A., Mokhtarzade, M., & Ebadi, H. (2011). Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2011.2144607
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.
