An improved AKAZE algorithm for UAV image feature matching in debris flow area

doi:10.13474/j.cnki.11-2246.2023.0046

Abstract

Abstract: To solve the problem of low timeliness when UAV high-resolution image feature matching is used in debris flow disasters, an improved AKAZE UAV image feature matching algorithm is proposed in this paper. The AKAZE feature point detection algorithm is used to extract locally stable invariant features, and the binary descriptor BEBLID is used to describe the detected feature points. Then, the nearest neighbor distance ratio (NNDR) is used for preliminary matching. Furthermore, epipolar geometric constraint is used to calculate the transformation matrix to purify the inner points and improve matching quality. Five groups of UAV sequences images are selected for the feature-matching experiment and compared with the classic SIFT algorithm, AKAZE algorithm, and ORB algorithm. The experimental results show that the matching accuracy of the proposed method is close to that of SIFT algorithm, slightly higher than that of the AKAZE algorithm, significantly better than that of the ORB algorithm, and the calculation speed is significantly better than that of SIFT algorithm and AKAZE algorithm, basically reaching the calculation efficiency of ORB algorithm. The method proposed in this paper can be applied to the UAV image data processing in the debris flow scenes which requires high matching accuracy and matching time.

Key words: UAV images in debris flow area, feature extraction, boosted efficient binary local image descriptor, epipolar geometric constraint, image matching

CLC Number:

P237

ZONG Huilin, YUAN Xiping, GAN Shu, ZHANG Xiaolun, LIANG Changxian, ZHAO Zhenfeng. An improved AKAZE algorithm for UAV image feature matching in debris flow area[J]. Bulletin of Surveying and Mapping, 2023, 0(2): 91-96,103.

References

[1] 张祖勋, 张剑清. 数字摄影测量学[M]. 2版. 武汉: 武汉大学出版社, 2012: 226-229.
[2] 徐芳, 邓非. 数字摄影测量学基础[M]. 武汉: 武汉大学出版社, 2017: 221-233.
[3] LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[4] BAY H, ESS A, TUYTELAARS T, et al. Speeded-up robust features (SURF)[J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359.
[5] RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: an efficient alternative to SIFT or SURF[C]//Proceedings of International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011: 2564-2571.
[6] HARRIS C, STEPHENS M. A combined corner and edge detector[C]//Proceedings of the Alvey Vision Conference. Manchester: Alvey Vision Club, 1988.
[7] ALCANTARILLA P F, BARTOLI A, DAVISON A J. KAZE features[C]//Proceedings of the 12th European Conference on Computer Vision-Volume Part VI. New York: ACM, 2012: 214-227.
[8] ALCANTARILLA P, NUEVO J, BARTOLI A. Fast explicit diffusion for accelerated features in nonlinear scale spaces[C]//Proceedings of the British Machine Vision Conference. Bristol: British Machine Vision Association, 2013.
[9] 李闯, 王晓红, 何志伟. 利用A-AKAZE算法进行喀斯特地区无人机影像匹配[J]. 测绘通报, 2020(6): 53-56.
[10] 梁焕青, 谢意, 付四洲. 颜色不变量与AKAZE特征相结合的无人机影像匹配算法[J]. 测绘学报, 2017, 46(7): 900-909.
[11] 薄单, 李宗春, 王晓南, 等. 适用于倾斜影像的加速KAZE-SIFT特征提取算法[J]. 计算机应用, 2019, 39(7): 2093-2097.
[12] LI Dan, XU Qiannan, YU Wennian, et al. SRP-AKAZE: an improved accelerated KAZE algorithm based on sparse random projection[J]. IET Computer Vision, 2020, 14(4): 131-137.
[13] 沈学利, 陈鑫彤. 三元组描述符的特征匹配算法[J]. 计算机应用与软件, 2019, 36(2): 205-210.
[14] 李瑞祥,赵海涛,葛小三,等.多匹配策略融合的无人机影像匹配方法[J].测绘科学,2021,46(3):87-95.
[15] SUÁREZ I, SFEIR G, BUENAPOSADA J M, et al. BEBLID: Boosted efficient binary local image descriptor[J]. Pattern Recognition Letters, 2020, 133: 366-372.
[16] 季顺平. 智能摄影测量学导论[M]. 北京: 科学出版社, 2018: 99-100.
[17] 詹总谦, 李一挥, 王陈东, 等. 顾及局部相对几何变形改正的影像匹配和空三逐步精化方法[J]. 武汉大学学报(信息科学版), 2018, 43(11): 1620-1627.
[18] 董杨, 范大昭, 纪松, 等. 主成分分析的匹配点对提纯方法[J]. 测绘学报, 2017, 46(2): 228-236.