A new band registration method for coated dual-lens multispectral camera

doi:10.7523/j.ucas.2020.0021

Abstract

Abstract: In order to solve the problem of band registration of coated dual lens multispectral camera, a fast registration method based on improved SURF (speeded up robust features) algorithm is proposed. Firstly, the distortion of each band image is corrected. Then, the fast version of SURF algorithm (F-SURF) is used to register the undistorted band images. Compared with the original SURF algorithm, the F-SURF algorithm optimizes the number of octave, discards the rotation invariance and introduces the PROSAC algorithm. The experimental results indicate that the registration accuracy of the F-SURF algorithm is better than that of the classic SURF algorithm, and the time efficiency is significantly improved.

Key words: coated multispectral camera, registration, SURF, F-SURF

CLC Number:

P237

ZHENG Jiayi, FANG Junyong, WANG Xiao, ZHANG Xiaohong, LIU Xue. A new band registration method for coated dual-lens multispectral camera[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(3): 377-385.

References

[1] 赵英时.遥感应用分析原理与方法[M]. 北京:科学出版社, 2003: 38.
[2] Lowe D G. Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision, 2004, 60(2): 91-110. DOI:10.1023/b:visi.0000029664.99615.94.
[3] 王峰,尤红建,傅兴玉,等.应用于多源SAR图像匹配的级联SIFT算法[J].电子学报, 2016, 44(3): 548-554. DOI:10.3969/j.issn.0372-2112.2016.03.008.
[4] 陈梦婷,闫冬梅,王刚.基于Harris角点和SIFT描述符的高分辨率遥感影像匹配算法[J].中国图象图形学报, 2012, 17(11): 1453-1459.
[5] Bay H,Tuytelaars T,Van G L. SURF:speeded up robust features [C]//European Conference on Computer Vision-ECCV 2006.Springer, 2006: 404-417. DOI:10.1007/11744023-32.
[6] 罗天健,刘秉瀚.融合特征的快速SURF配准算法[J].中国图象图形学报, 2015, 20(1): 95-103. DOI:10.11834/jig.20150110.
[7] 孙越,王宏琦,李峰,等.面向变化检测的遥感影像弹性配准方法[J].武汉大学学报 ·信息科学版, 2018, 43(1): 53-59. DOI:10.13203/j.whugis20150510.
[8] Rosten E,Drummond T. Machine learning for high-speed corner detection [C]//European Conference on Computer Vision-ECCV 2006.Springer, 2006: 430-443. DOI:10.1007/11744023_34.
[9] Calonder M, Lepetit V, Strecha C, et al. BRIEF:Binary Robust Independent Elementary Features [C]//European Conference on Computer Vision-ECCV 2010. Springer, 2010: 778-792. DOI:10.1007/978-3-642-15561-1_56.
[10] Ke Y,Sukthankar R. PCA-SIFT:a more distinctive representation for local image descriptors [C]//IEEE Conference on Computer Vision and Pattern Recognition-CVPR 2004. June 27-July 2, 2004, Washington, DC, USA. IEEE, 2004: 511-517. DOI:10.1109/CVPR.2004.1315206.
[11] Rublee E,Rabaud V,Konolige K,et al. ORB: an efficient alternative to SIFT or SURF [C]// 2011 IEEE International Conference on Computer Vision. November 6-13, 2011, Barcelona, Spain. IEEE, 2011: 2564-2571. DOI:10.1109/ICCV.2011.6126544.
[12] Leutenegger S,Chli M,Siegwart R Y. Brisk: Binary robust invariant scalable keypoints [C]// 2011 IEEE International Conference on Computer Vision. November 6-13, 2011, Barcelona, Spain. IEEE, 2011: 2548-2555. DOI:10.1109/ICCV.2011.6126542.
[13] Alahi A,Ortiz R,Vandergheynst P. FREAK: Fast retina keypoint [C]// 2012 IEEE Conference on Computer Vision and Pattern Recognition June 16-21, 2012, Providence, RI, USA. IEEE, 2012: 510-517. DOI:10.1109/CVPR.2012.6247715.
[14] 许允喜,陈方.局部图像描述符最新研究进展[J].中国图象图形学报, 2015, 20(9): 1133-1150. DOI:10.11834/jig.20150901.
[15] 冯文灏,商浩亮,侯文广.影像的数字畸变模型[J].武汉大学学报 ·信息科学版, 2006, 31(2):99-103. DOI:10.3969/j.issn.1671-8860.2006.02.002.
[16] 冯文灏.近景摄影测量:物体外形与运动状态的摄影法测定[M].武汉:武汉大学出版社, 2002: 116-142.
[17] 闫敬文,王宏志,林哲,等.基于改进SURF的遥感图像目标识别[J].扬州大学学报(自然科学版), 2018, 21(3): 74-78. DOI:10.19411/j.1007-824x.2018.03.016.
[18] 董强,刘晶红,周前飞.用于遥感图像拼接的改进SURF算法[J].吉林大学学报(工学版), 2017, 47(5): 1644-1652. DOI:10.13229/j.cnki.jdxbgxb201705042.
[19] Hossein-Nejad Z, Nasri M. An adaptive image registration method based on SIFT features and RANSAC transform[J].Computers & Electrical Engineering, 2016, 62:1-14. DOI:10.1016/j.compeleceng.2016.11.034.
[20] 迟英朋,刘畅.一种适用于SAR图像配准的改进SIFT算法[J].中国科学院大学学报, 2019, 36(2): 259-266. DOI:10.7523/j.issn.2095-6134.2019.02.014.
[21] Chum O,Matas J. Matching with PROSAC: progressive sample consensus [C]// 2005 IEEE Conference on Computer Vision and Pattern Recognition. June 20-25, 2005, San Diego, CA, USA. IEEE, 2005: 220-226. DOI:10.1109/CVPR.2005.221.
[22] Zhao P P, Ding D R, Wang Y X, et al. An improved GMS-PROSAC algorithm for image mismatch elimination[J]. Systems Science & Control Engineering, 2018, 6(1): 220-229. DOI:10.1080/21642583.2018.1477635.
[23] 李振宇,田源,陈方杰,等.基于改进ORB和PROSAC的无人机航拍图像拼接算法[J].激光与光电子学进展, 2019, 56(23): 91-99. DOI:10.3788/LOP56.231003.
[24] 产叶林,胡新平.基于FAST和SURF的特征点快速匹配算法[J].计算机工程与设计, 2019, 40(12): 3500-3504. DOI:10.16208/j.issn1000-7024.2019.12.023.