Advanced search
Publication Cover
Remote Sensing Letters Volume 13, 2022 - Issue 11
Submit an article Journal homepage
239
Views
1
CrossRef citations to date
0
Altmetric
Research Article

An efficient SIFT-based matching algorithm for optical remote sensing images

Sourabh Paula School of Electronics Engineering (SENSE), Vellore Institute of Technology (VIT), Chennai, IndiaCorrespondence[email protected]
,
Udaysankar D.b Department of Electronics and Communication Engineering, Madanapalle Institute of Technology and Science (MITS), Madanapalle, India
,
Yashwanth Naidub Department of Electronics and Communication Engineering, Madanapalle Institute of Technology and Science (MITS), Madanapalle, India
&
Yogeswara Reddyb Department of Electronics and Communication Engineering, Madanapalle Institute of Technology and Science (MITS), Madanapalle, India
Pages 1069-1079 | Received 24 Mar 2022, Accepted 26 Aug 2022, Published online: 14 Sep 2022

References

  • Bay, H., A. Ess, T. Tuytelaars, and L. Van Gool. 2008. “Speeded-Up Robust Features (SURF).” Computer Vision and Image Understanding 110 (3): 346–359. doi:10.1016/j.cviu.2007.09.014.
  • Fan, B., C. Huo, C. Pan, and Q. Kong. 2013. “Registration of Optical and SAR Satellite Images by Exploring the Spatial Relationship of the Improved SIFT.” IEEE Geoscience and Remote Sensing Letters 10 (4): 657–661. doi:10.1109/LGRS.2012.2216500.
  • Fan, B., F. Wu, and Z. Hu. 2011. “Aggregating Gradient Distributions into Intensity Orders: A Novel Local Image Descriptor.” Proceedings of the CVPR, Colorado Springs, CO, IEEE, 2377–2384.
  • Goncalves, H., L. Corte-Real, and J. A. Goncalves. 2011. “Automatic Image Registration Through Image Segmentation and SIFT.” IEEE Transactions on Geoscience and Remote Sensing 49 (7): 2589–2600. doi:10.1109/TGRS.2011.2109389.
  • Gonçalves, H., J. A. Gonçalves, and L. Corte-Real. 2009. “Measures for an Objective Evaluation of the Geometric Correction Process Quality.” IEEE Geoscience and Remote Sensing Letters 6 (2): 292–296. doi:10.1109/LGRS.2008.2012441.
  • Hasan, M., M. R. Pickering, and X. Jia. 2012. “Modified SIFT for Multi-Modal Remote Sensing Image Registration.” Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Munich, Germany, July. 2348–2351.
  • Kupfer, B., N. S. Netanyahu, and I. Shimshoni. 2014. “An Efficient SIFT-Based Mode-Seeking Algorithm for Sub-Pixel Registration of Remotely Sensed Images.” IEEE Geoscience and Remote Sensing Letters 12 (2): 379–383. doi:10.1109/LGRS.2014.2343471.
  • Li, J., Q. Hu, and M. Ai. 2019a. “LAM: Locality Affine-Invariant Feature Matching.” ISPRS Journal of Photogrammetry and Remote Sensing 154: 28–40. doi:10.1016/j.isprsjprs.2019.05.006.
  • Li, J., Q. Hu, and M. Ai. 2019b. “RIFT: Multi-Modal Image Matching Based on Radiation-Variation Insensitive Feature Transform.” IEEE Transactions on Image Processing 29: 3296–3310. doi:10.1109/TIP.2019.2959244.
  • Li, J., W. Xu, P. Shi, Y. Zhang, and Q. Hu. 2022. “LNIFT: Locally Normalized Image for Rotation Invariant Multimodal Feature Matching.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–14.
  • Lowe, D. G. 2004. “Distinctive Image Features from Scale-Invariant Keypoints.” International Journal of Computer Vision 60 (2): 91–110. doi:10.1023/B:VISI.0000029664.99615.94.
  • Ma, W., Z. Wen, Y. Wu, L. Jiao, M. Gong, Y. Zheng, and L. Liu. 2017. “Remote Sensing Image Registration with Modified SIFT and Enhanced Feature Matching.” IEEE Geoscience and Remote Sensing Letters 14 (1): 3–7. doi:10.1109/LGRS.2016.2600858.
  • Paul, S., and U. C. Pati. 2016. “Remote Sensing Optical Image Registration Using Modified Uniform Robust SIFT.” IEEE Geoscience and Remote Sensing Letters 13 (9): 1300–1304. doi:10.1109/LGRS.2016.2582528.
  • Sedaghat, A., and H. Ebadi. 2015. “Remote Sensing Image Matching Based on Adaptive Binning SIFT Descriptor.” IEEE Transactions on Geoscience and Remote Sensing : A Publication of the IEEE Geoscience and Remote Sensing Society 53 (10): 5283–5293. doi:10.1109/TGRS.2015.2420659.
  • Sedaghat, A., M. Mokhtarzade, and H. Ebadi. 2011. “Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 49 (11): 4516–4527. doi:10.1109/TGRS.2011.2144607.
  • Wang, S., H. You, and K. Fu. 2012. “BFSIFT: A Novel Method to Find Feature Matches for SAR Image Registration.” IEEE Geoscience and Remote Sensing Letters 9 (4): 649–653. doi:10.1109/LGRS.2011.2177437.
  • Wang, F., H. You, and X. Fu. 2015. “Adapted Anisotropic Gaussian SIFT Matching Strategy for SAR Registration.” IEEE Geoscience and Remote Sensing Letters 12 (1): 160–164. doi:10.1109/LGRS.2014.2330593.
  • Wu, Y., W. Ma, M. Gong, L. Su, and L. Jiao. 2015. “A Novel Point-Matching Algorithm Based on Fast Sample Consensus for Image Registration.” IEEE Geoscience and Remote Sensing Letters 12 (1): 43–47. doi:10.1109/LGRS.2014.2325970.
  • Yi, Z., C. Zhiguo, and X. Yang. 2008. “Multi-Spectral Remote Image Registration Based on SIFT.” Electronics Letters 44 (2): 107–108. doi:10.1049/el:20082477.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.