Robust and Fast Aerial and Satellite Image Matching based on Selective Scale and Rotation
Subject Areas : electrical and computer engineeringM. Safdari 1 , P. Moallem 2 , M. Sattari 3
1 -
2 -
3 -
Keywords: Keypoints descriptor Sobel, SIFT rBREIF bilateral image matching RANSAC,
Abstract :
SIFT method is used to extract keypoints of the image in order to overcome the problems of matching between the satellite and aerial images, including: difference in scale, rotation, brightness intensity and the geometric shape. Unfortunately, SIFT method extracts several unfavorable keypoints of satellite and aerial images because of the turbulence and the environmental factors which leads to unreliable matching and increasing complexity. In order to improve the quality of the extracted specific areas and the run time of the algorithm, first the edges of the original images are extracted by Sobel operator and thresholding, then by using the SIFT method, keypoints are extracted from the edge image. After extracting keypoints, using the rBREIF method, that have stability dependence with respect to atmospheric turbulence and rotation, descriptor for every point of the extracted points is created. Then by applying the bilateral image matching and the RANSAC method that removes the unfavorable adaptive points, the correct matching between the satellite and aerial images are found using the suggested method. The results of the proposed method on the real images show the superiority of this method in term of the accuracy and speed, compared to the some well-known matching methods such as SIFT.
[1] A. Nithya, et al., "Feature based automated aerial image to satellite image registration," International J. of Computer Science & Engineering Technology, vol. 3, no. 7, pp. 286-289, Jul. 2012.
[2] M. Guerrero, A Comparative Study of Three Image Matcing Algorithms: Sift, Surf, and Fast, Ph. D. Thesis, Utah State University, 2011.
[3] C. Harris and M. Stephens, "A combined corner and edge detector," in Proc. Fourth Alvey Vision Conf., pp. 147-151, 1988.
[4] M. Trajkovic and M. Hedley, "Fast corner detection," Image and Vision Computing J., vol. 16, no. 6, pp. 75-87, Feb. 1998.
[5] D. G. Lowe, "Object recognition from local scale-invariant features," in Proc. IEEE Computer Vision Conf., vol. 2, pp. 1150-1157, 20-25 Sep. 1999.
[6] Y. Ke and R. Sukthankar, "PCA-SIFT: a more distinctive representation for local image descriptors," in Proc. IEEE Computer Vision and Pattern Recognition Conf., pp. 511-517, 27 Jun. - 2 Jul. 2004.
[7] Y. Qing, B. Lin, and W. Fang, "An improved SIFT algorithm," J. of Harbin Engineering University, vol. 31, pp. 560-564, 2010.
[8] H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, " Speeded-up robust features (SURF)," Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, Jun. 2008.
[9] M. Calonder, et al., "BRIEF: computing a local binary descriptor very fast," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 34, no. 7, pp. 1287-1298, Jul. 2012.
[10] E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "ORB: an effcient alternative to SIFT or SURF," in Proc. 13th IEEE Int. Conf. on Computer Vision, ICCV'11, pp. 2564-2571, 6-13. Nov. 2011.
[11] D. G. Lowe, "Distinctive image features from scale invariant keypoints," International J. of Computer Vision, Springer, vol. 60, no. 2, pp. 91-110, Nov. 2004.
[12] X. Lu, S. Zhang, W. Yang, and Y. Chen, "SIFT and shape information incorporated into fluid model for non-rigid registration of ultrasound images," J. of Computer Methods and Programs in Biomedicine, vol. 100, no. 2, pp. 123-131, Nov. 2010.
