Remote Sensing Image Registration based on a Geometrical Model Matching
Subject Areas : Signal ProcessingZahra Hossein-Nejad 1 , Hamed Agahi 2 * , Azar Mahmoodzadeh 3
1 - Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
2 - Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
3 - Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
Keywords: SIFT, Matching Method, Remote Sensing Image Registration, Transformation Model,
Abstract :
Remote sensing image registration is the method of aligning two images from the same scene taken under different imaging circumstances containing different times, angles, or sensors. Scale-invariant feature transform (SIFT) is one of the most common matching methods previously used in the remote sensing image registration. The defects of SIFT are the large number of mismatches and high execution time due to the high dimensions of classical SIFT descriptor. These drawbacks reduce the efficiency of the SIFT algorithm. To enhance the performance of the remote sensing image registration, this paper proposes an approach consisting of three different steps. At first, the keypoints of both reference and second images are extracted using SIFT algorithm. Then, to increase the speed of the algorithm and accuracy of the matching, the SIFT descriptor with the vector length of 64 is used for keypoints description. Finally, a new method has been proposed for the image matching. The proposed matching method is based on calculating the distances of keypoints and their transformed points. Simulation results of applying the proposed method to some standard databases demonstrated the superiority of this approach compared with some other existing methods, according to the root mean square error (RMSE), precision and running time criteria.
[1] A. Wong and D. Clausi, "ARRSI: automatic registration of remote-sensing images," Geoscience and Remote Sensing, IEEE Transactions on, vol. 45, pp. 1483-1493, 2007.
[2] W.-J. Lee and S.-J. Oh, "Remote sensing image registration using equivariance features," in 2021 International Conference on Information Networking (ICOIN), 2021, pp. 776-781.
[3] Z. Hossein-Nejad and M. Nasri, "A New Method in Image Matching Based on Spatial Relationships in Multi-Sensor Remote Sensing Images " Iranian Remote Sensing & GIS, pp. 73-94, 2018.
[4] Y. Liu, H. Cao, Y. Zhao, Q. He, Y. Yang, L. Wang, et al., "A Remote sensing image registration algorithm based on multiple constraints and a variational Bayesian framework," Remote Sensing Letters, vol. 12, pp. 296-305, 2021.
[5] Z. Hossein-Nejad and M. Nasri, "A Review on Image Registration Methods, Concepts and applications," Journal of Machine Vision and Image Processing, pp. 39-67, 2017.
[6] W. Lee, D. Sim, and S.-J. Oh, "A CNN-Based High-Accuracy Registration for Remote Sensing Images," Remote Sensing, vol. 13, p. 1482, 2021.
[7] H.-M. Chen, M. K. Arora, and P. K. Varshney, "Mutual information-based image registration for remote sensing data," International Journal of Remote Sensing, vol. 24, pp. 3701-3706, 2003.
[8] X. Xie, Y. Zhang, X. Ling, and X. Wang, "A novel extended phase correlation algorithm based on Log-Gabor filtering for multimodal remote sensing image registration," International Journal of Remote Sensing, vol. 40, pp. 5429-5453, 2019.
[9] M. I. Patel, V. K. Thakar, and S. K. Shah, "Image registration of satellite images with varying illumination level using HOG descriptor based SURF," Procedia computer science, vol. 93, pp. 382-388, 2016.
[10] P. Schwind, S. Suri, P. Reinartz, and A. Siebert, "Applicability of the SIFT operator to geometric SAR image registration," International Journal of Remote Sensing, vol. 31, pp. 1959-1980, 2010.
[11] G. Sreeja and O. Saraniya, "A Comparative Study on Image Registration Techniques for SAR Images," in 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 2019, pp. 947-953.
[12] Y. Ye, J. Shan, L. Bruzzone, and L. Shen, "Robust registration of multimodal remote sensing images based on structural similarity," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, pp. 2941-2958, 2017.
[13] 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, pp. 4516-4527, 2011.
[14] Z. Hossein-Nejad and M. Nasri, "A-RANSAC: Adaptive random sample consensus method in multimodal retinal image registration," Biomedical Signal Processing and Control, vol. 45, pp. 325-338, 2018.
[15] C. Harris and M. Stephens, "A combined corner and edge detector," in Alvey vision conference, 1988, p. 50.
[16] D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, vol. 60, pp. 91-110, 2004.
[17] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, "Speeded-up robust features (SURF)," Computer vision and image understanding, vol. 110, pp. 346-359, 2008.
[18] Z. Hossein-Nejad and M. Nasri, "RKEM: Redundant Keypoint Elimination Method in Image Registration," IET Image Processing, vol. 11, pp. 273-284, 2017.
[19] A. Sedaghat and N. Mohammadi, "High-resolution image registration based on improved SURF detector and localized GTM," International Journal of Remote Sensing, vol. 40, pp. 2576-2601, 2019.
[20] A. Sedaghat and H. Ebadi, "Remote sensing image matching based on adaptive binning SIFT descriptor," IEEE transactions on geoscience remote Sensing Technology and Application, vol. 53, pp. 5283-5293, 2015.
[21] M. Hasan, M. R. Pickering, and X. Jia, "Modified SIFT for multi-modal remote sensing image registration," in 2012 IEEE International Geoscience and Remote Sensing Symposium, 2012, pp. 2348-2351.
[22] B. Kupfer, N. S. Netanyahu, and I. Shimshoni, "An Efficient SIFT-Based Mode-Seeking Algorithm for Sub-Pixel Registration of Remotely Sensed Images," Geoscience and Remote Sensing Letters, IEEE, vol. 12, pp. 379-383, 2015.
[23] Z. Yi, C. Zhiguo, and X. Yang, "Multi-spectral remote image registration based on SIFT," Electronics Letters, vol. 44, pp. 107-108, 2008.
[24] K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 27, pp. 1615-1630, 2005.
[25] N. Y. Khan, B. McCane, and G. Wyvill, "SIFT and SURF performance evaluation against various image deformations on benchmark dataset," in 2011 International Conference on Digital Image Computing: Techniques and Applications, 2011, pp. 501-506.
[26] R. Bouchiha and K. Besbes, "Comparison of local descriptors for automatic remote sensing image registration," Signal, Image and Video Processing, vol. 9, pp. 463-469, 2015.
[27] M. Deshmukh and U. Bhosle, "A survey of image registration," International Journal of Image Processing (IJIP), vol. 5, p. 245, 2011.
[28] M. Hasan, X. Jia, A. Robles-Kelly, J. Zhou, and M. R. Pickering, "Multi-spectral remote sensing image registration via spatial relationship analysis on sift keypoints," in Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, 2010, pp. 1011-1014.
[29] S. Saxena and R. K. Singh, "A survey of recent and classical image registration methods," International journal of signal processing, image processing and pattern recognition, vol. 7, pp. 167-176, 2014.
[30] S. Chen, S. Zheng, Z. Xu, C. Guo, and X. Ma, "AN IMPROVED IMAGE MATCHING METHOD BASED ON SURF ALGORITHM," International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, vol. 42, 2018.
[31] P. Etezadifar and H. Farsi, "A New Sample Consensus Based on Sparse Coding for Improved Matching of SIFT Features on Remote Sensing Images," IEEE Transactions on Geoscience and Remote Sensing, 2020.
[32] W. He and X. Deng, "A modified SUSAN corner detection algorithm based on adaptive gradient threshold for remote sensing image," in 2010 International Conference on Optoelectronics and Image Processing, 2010, pp. 40-43.
[33] Z. Hossein-Nejad and M. Nasri, "An adaptive image registration method based on SIFT features and RANSAC transform," Computers & Electrical Engineering, vol. 62, pp. 524-537, 2017.