1. [1] S. Li, M. Peng, B. Zhang, X. Feng, and Y. Wu, "Auto-registration of medium and high spatial resolution satellite images by integrating improved SIFT and spatial consistency constraints," International Journal of Remote Sensing, vol. 40, no. 14, pp. 5635-5650, 2019. [ DOI:10.1080/01431161.2019.1580793] 2. [2] B. Chaudhuri, B. Demir, S. Chaudhuri, and L. Bruzzone, "Multilabel remote sensing image retrieval using a semisupervised graph-theoretic method," IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 2, pp. 1144-1158, 2017. [ DOI:10.1109/TGRS.2017.2760909] 3. [3] J. Dai, W. Song, L. Pei, and J. Zhang, "Remote sensing image matching via Harris detector and SIFT discriptor," in 2010 3rd International Congress on Image and Signal Processing, 2010, vol. 5: IEEE, pp. 2221-2224. [ DOI:10.1109/CISP.2010.5647782] 4. [4] E. G. Parmehr, C. S. Fraser, C. Zhang, and J. Leach, "Automatic registration of optical imagery with 3D LiDAR data using statistical similarity," ISPRS Journal of photogrammetry and remote sensing, vol. 88, pp. 28-40, 2014. [ DOI:10.1016/j.isprsjprs.2013.11.015] 5. [5] J. Ma, X. Jiang, A. Fan, J. Jiang, and J. Yan, "Image matching from handcrafted to deep features: A survey," International Journal of Computer Vision, vol. 129, no. 1, pp. 23-79, 2021. [ DOI:10.1007/s11263-020-01359-2] 6. [6] Y. Han, S. Jung, S. Liu, and J. Yeom, "Effect analysis in the fine co-registration of very-high-resolution satellite images for unsupervised change detection," in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, 2019: IEEE, pp. 1558-1561. [ DOI:10.1109/IGARSS.2019.8898916] 7. [7] F. Song et al., "Multi-scale feature based land cover change detection in mountainous terrain using multi-temporal and multi-sensor remote sensing images," IEEE Access, vol. 6, pp. 77494-77508, 2018. [ DOI:10.1109/ACCESS.2018.2883254] 8. [8] B. Ayhan, M. Dao, C. Kwan, H.-M. Chen, J. F. Bell, and R. Kidd, "A novel utilization of image registration techniques to process mastcam images in mars rover with applications to image fusion, pixel clustering, and anomaly detection," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 10, pp. 4553-4564, 2017. [ DOI:10.1109/JSTARS.2017.2716923] 9. [9] Y. Zhou, A. Rangarajan, and P. D. Gader, "An integrated approach to registration and fusion of hyperspectral and multispectral images," IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 5, pp. 3020-3033, 2019. [ DOI:10.1109/TGRS.2019.2946803] 10. [10] Z. Li, J. Yue, and L. Fang, "Adaptive Regional Multiple Features for Large-Scale High-Resolution Remote Sensing Image Registration," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13, 2022. [ DOI:10.1109/TGRS.2022.3141101] 11. [11] C. Leng, H. Zhang, B. Li, G. Cai, Z. Pei, and L. He, "Local feature descriptor for image matching: A survey," IEEE Access, vol. 7, pp. 6424-6434, 2018. [ DOI:10.1109/ACCESS.2018.2888856] 12. [12] S. Suri and P. Reinartz, "Mutual-information-based registration of TerraSAR-X and Ikonos imagery in urban areas," IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 2, pp. 939-949, 2009. [ DOI:10.1109/TGRS.2009.2034842] 13. [13] X. Dai and S. Khorram, "A feature-based image registration algorithm using improved chain-code representation combined with invariant moments," IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no. 5, pp. 2351-2362, 1999. [ DOI:10.1109/36.789634] 14. [14] A. Sedaghat and H. Ebadi, "Very high resolution image matching based on local features and k‐means clustering," The Photogrammetric Record, vol. 30, no. 150, pp. 166-186, 2015. [ DOI:10.1111/phor.12101] 15. [15] N. Jovhari, A. Sedaghat, and N. Mohammadi, "Performance Evaluation of Local Detectors in the Presence of Noise for Multi-Sensor Remote Sensing Image Matching," Engineering Journal of Geospatial Information Technology, vol. 10, no. 2, pp. 63-88, 2022. [ DOI:10.52547/jgit.10.2.63] 16. [16] G. Lowe, "Sift-the scale invariant feature transform," Int. J, vol. 2, no. 91-110, p. 2, 2004. 17. [17] X. Shen and W. Bao, "The remote sensing image matching algorithm based on the normalized cross-correlation and sift," Journal of the Indian Society of Remote Sensing, vol. 42, no. 2, pp. 417-422, 2014. [ DOI:10.1007/s12524-013-0323-y] 18. [18] H.-H. Chang, G.-L. Wu, and M.-H. Chiang, "Remote sensing image registration based on modified SIFT and feature slope grouping," IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 9, pp. 1363-1367, 2019. [ DOI:10.1109/LGRS.2019.2899123] 19. [19] H. Zhang et al., "Remote Sensing Image Registration Based on Local Affine Constraint With Circle Descriptor," IEEE Geoscience and Remote Sensing Letters, 2020. 20. [20] M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, vol. 24, no. 6, pp. 381-395, 1981. [ DOI:10.1145/358669.358692] 21. [21] Z. Yi, C. Zhiguo, and X. Yang, "Multi-spectral remote image registration based on SIFT," Electronics Letters, vol. 44, no. 2, pp. 107-108, 2008. [ DOI:10.1049/el:20082477] 22. [22] H. Yang, X. Li, Y. Ma, L. Zhao, and S. Chen, "A High Precision Feature Matching Method Based on Geometrical Outlier Removal for Remote Sensing Image Registration," IEEE Access, vol. 7, pp. 180027-180038, 2019. [ DOI:10.1109/ACCESS.2019.2951796] 23. [23] X. Chang, S. Du, Y. Li, and S. Fang, "A coarse-to-fine geometric scale-invariant feature transform for large size high resolution satellite image registration," Sensors, vol. 18, no. 5, p. 1360, 2018. [ DOI:10.3390/s18051360] 24. [24] A. Sedaghat, M. Mokhtarzade, and H. Ebadi, "Uniform robust scale-invariant feature matching for optical remote sensing images," IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 11, pp. 4516-4527, 2011. [ DOI:10.1109/TGRS.2011.2144607] 25. [25] Z. Ghassabi, J. Shanbehzadeh, A. Sedaghat, and E. Fatemizadeh, "An efficient approach for robust multimodal retinal image registration based on UR-SIFT features and PIIFD descriptors," EURASIP Journal on Image and Video Processing, vol. 2013, no. 1, p. 25, 2013. [ DOI:10.1186/1687-5281-2013-25] 26. [26] K. Mikolajczyk et al., "A comparison of affine region detectors," International journal of computer vision, vol. 65, no. 1-2, pp. 43-72, 2005. [ DOI:10.1007/s11263-005-3848-x] 27. [27] Z. Hossein-Nejad, H. Agahi, and A. Mahmoodzadeh, "Remote Sensing Image Registration based on a Geometrical Model Matching," Journal of Information Systems and Telecommunication (JIST), vol. 5, no. 36, p. 41, 2021. [ DOI:10.52547/jist.9.36.41] 28. [28] W. Ma et al., "Remote sensing image registration with modified SIFT and enhanced feature matching," IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 1, pp. 3-7, 2016. [ DOI:10.1109/LGRS.2016.2600858] 29. [29] A. Sedaghat and H. Ebadi, "Remote sensing image matching based on adaptive binning SIFT descriptor," IEEE transactions on geoscience and remote sensing, vol. 53, no. 10, pp. 5283-5293, 2015. [ DOI:10.1109/TGRS.2015.2420659] 30. [30] L. Zhang, L. Zhang, and B. Du, "Deep learning for remote sensing data: A technical tutorial on the state of the art," IEEE Geoscience and Remote Sensing Magazine, vol. 4, no. 2, pp. 22-40, 2016. [ DOI:10.1109/MGRS.2016.2540798] 31. [31] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014. 32. [32] C. Szegedy et al., "Going deeper with convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1-9. [ DOI:10.1109/CVPR.2015.7298594] 33. [33] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778. [ DOI:10.1109/CVPR.2016.90] 34. [34] L. Zheng, Y. Yang, and Q. Tian, "SIFT meets CNN: A decade survey of instance retrieval," IEEE transactions on pattern analysis and machine intelligence, vol. 40, no. 5, pp. 1224-1244, 2017. [ DOI:10.1109/TPAMI.2017.2709749] 35. [35] Z. Yang, T. Dan, and Y. Yang, "Multi-temporal remote sensing image registration using deep convolutional features," IEEE Access, vol. 6, pp. 38544-38555, 2018. [ DOI:10.1109/ACCESS.2018.2853100] 36. [36] F. Ye, Y. Su, H. Xiao, X. Zhao, and W. Min, "Remote sensing image registration using convolutional neural network features," IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 2, pp. 232-236, 2018. [ DOI:10.1109/LGRS.2017.2781741] 37. [37] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017. [ DOI:10.1145/3065386] 38. [38] H. Yang, X. Li, L. Zhao, and S. Chen, "A novel coarse-to-fine scheme for remote sensing image registration based on SIFT and phase correlation," Remote Sensing, vol. 11, no. 15, p. 1833, 2019. [ DOI:10.3390/rs11151833]
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