高分辨率遥感图像SIFT和SURF算法匹配性能研究

齐冰洁; 刘金国; 张博研; 左洋; 吕世良

doi:10.3788/CO.20171003.0331

Volume 10 Issue 3

May 2017

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Article Contents

Chinese Optics > 2017 > 10(3): 331-339.

QI Bing-jie, LIU Jin-guo, ZHANG Bo-yan, ZUO Yang, LYU Shi-liang. Research on matching performance of SIFT and SURF algorithms for high resolution remote sensing image[J]. Chinese Optics, 2017, 10(3): 331-339. doi: 10.3788/CO.20171003.0331

Citation:

QI Bing-jie, LIU Jin-guo, ZHANG Bo-yan, ZUO Yang, LYU Shi-liang. Research on matching performance of SIFT and SURF algorithms for high resolution remote sensing image[J]. Chinese Optics, 2017, 10(3): 331-339. doi: 10.3788/CO.20171003.0331

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Research on matching performance of SIFT and SURF algorithms for high resolution remote sensing image

doi: 10.3788/CO.20171003.0331

1.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds:

National Natural Science Foundation of China 61405191

Received Date: 18 Jan 2017
Rev Recd Date: 27 Mar 2017
Publish Date: 01 Jun 2017

Abstract

Abstract

Image matching is the basis of image rectification and mosaic. Because of higher features similarity and smaller overlapped area than ordinary images, the remote sensing images have higher requirements on matching algorithm in both performance and iteration speed. The performances in three aspects:feature detection, feature description and feature matching, are analyzed between the SIFT algorithm and the SURF algorithm in terms of speed and accuracy. The requirements of the degree of overlapping between remote sensing images and the matching distance of the genvector is discussed as well. In view of the characteristic that SURF algorithm is sensitive to the error in feature detection, oSURF algorithm is presented in this paper. Finally, the advantages and disadvantages of each algorithm are analyzed by using satellite remote sensing data of level 1A. The results show that iteration speed of SURF algorithm is three times faster than SIFT algorithm. Under the same matching rate, the width of overlapped area on image required in SURF algorithm is 1.25 times of the dimension of genvector but 1.5 times in SIFT, and the accuracy of oSURF algorithm is increased by 5%~10% compared with SURF algorithm in the same computation speed, which indicate that oSURF is more suitable for remote sensing image stitching.
- remote sensing image,
- feature matching,
- SIFT,
- SURF,
- degree of overlapping

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References(20)

References

[1]	LOWE D G. Object recognition from local scale-invariant features[C]. International Conference on Computer Vision, Kerkyra, Greece:ICCV, 1999:1150-1157.
[2]	LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International J. Compute Vision, 2004, 60(60):91-110. http://nichol.as/papers/Lowe/Distinctive%20Image%20Features%20from%20Scale-Invariant.pdf
[3]	BAY H, TUYTELAARS T, GOOL L. SURF:speeded up robust features[J]. Computer Vision & Image Understanding, 2006, 110(3):404-417. http://dev.ipol.im/~morel/Dossier_MVA_2010_Cours_Transparents_Documents/Cours_4_document_SURF_paper.pdf
[4]	BAY H, ESS A, TUYTELAARS T, et al.. Speeded-Up Robust Features (SURF)[J]. Computer Vision & Image Understanding, 2008, 110(3):346-359. http://www.micc.unifi.it/delbimbo/wp-content/uploads/2011/03/reference_papers/Bay.pdf
[5]	LUO J, GWUN O. A Comparison of SIFT, PCA-SIFT and SURF[J]. J. Business Education, 2009, 3(4):143-152.
[6]	索春宝, 杨东清, 刘云鹏.多种角度比较SIFT、SURF、BRISK、ORB、FREAK算法[J].北京测绘, 2014, (4):23-26. http://www.cnki.com.cn/Article/CJFDTOTAL-BJCH201404006.htm SUO CH B, YANG D Q, LIU Y P. Comparison of SIFT, SURF, BRISK, ORB and FREAK algorithms in various aspects[J]. Beijing Surveying and Mapping, 2014, (4):23-26.(in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-BJCH201404006.htm
[7]	安维胜, 余让明, 伍玉铃.基于FAST和SURF的图像配准算法[J].计算机工程, 2015, 41(10):232-235. doi: 10.3969/j.issn.1000-3428.2015.10.043 AN W SH, YU R M, WU Y L. Image registration algorithm based on FAST and SURF[J]. Computer Engineering, 2015, 41(10):232-235.(in Chinese) doi: 10.3969/j.issn.1000-3428.2015.10.043
[8]	王新华, 黄玮, 欧阳继红.多探测器拼接成像系统实时图像配准[J].中国光学, 2015, 8(2):211-219. http://www.chineseoptics.net.cn/CN/abstract/abstract9257.shtml WANG X H, HUANG W, OUYANG J H. Real-time image registration of the multi-detectors mosaic imaging system[J]. Chinese Optics, 2015, 8(2):211-219.(in Chinese) http://www.chineseoptics.net.cn/CN/abstract/abstract9257.shtml
[9]	许佳佳.结合Harris与SIFT算子的图像快速配准算法[J].中国光学, 2015, 8(4):574-581. http://www.chineseoptics.net.cn/CN/abstract/abstract9285.shtml XU J J. Fast image registration method based on Harris and SIFT algorithm[J]. Chinese Optics, 2015, 8(4):574-581.(in Chinese) http://www.chineseoptics.net.cn/CN/abstract/abstract9285.shtml
[10]	张云生, 邹峥嵘.基于改进ORB算法的遥感图像自动配准方法[J].国土资源遥感, 2013, 25(3):20-24. doi: 10.6046/gtzyyg.2013.03.04 ZAHNG Y SH, ZOU ZH R. Automatic registration method for remote sensing images based on improved ORB algorithm[J]. Remote Sensing for Land and Resources, 2013, 25(3):20-24.(in Chinese) doi: 10.6046/gtzyyg.2013.03.04
[11]	罗楠, 孙权森, 耿蕾蕾, 等.一种扩展SURF描述符及其在遥感图像配准中的应用[J].测绘学报, 2013, 42(3):383-388. http://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201303012.htm LUO N, SUN Q S, GENG L L, et al.. An extended SURF descriptor and its application in remote sensing images registration[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(3):383-388.(in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201303012.htm
[12]	闫利, 陈林.一种改进的SURF及其在遥感影像匹配中的应用[J].武汉大学学报:信息科学版, 2013, 38(7):770-773. http://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201307005.htm YAN L, CHEN L. A modified SURF descriptor and its application in remote sensing images matching[J]. Geomatics and Information Science of Wuhan University, 2013, 38(7):770-773.(in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201307005.htm
[13]	葛盼盼, 陈强.基于SURF特征提取的遥感图像自动配准[J].计算机系统应用, 2014, 23(3):16-24. http://www.cnki.com.cn/Article/CJFDTOTAL-XTYY201403003.htm GE P P, CHEN Q. Remote sensing image automatic registration based on SURF feature extraction[J]. Computer Systems & Applications, 2014, 23(3):16-24.(in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-XTYY201403003.htm
[14]	LINDEBERG T. Scale-space theory:a basic tool for analyzing structures at different scales[J]. J. Applied Statistics, 1994, 21(1):225-270. doi: 10.1080/757582976
[15]	VIOLA P, JONES M. Rapid object detection using a boosted cascade of simple features[C]. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA, 2001:511-518
[16]	翟优, 曾峦, 熊伟.不同局部邻域划分加速鲁棒特征描述符的性能分析[J].光学精密工程, 2013, 21(9):2395-2404. http://www.cnki.com.cn/Article/CJFDTOTAL-GXJM201309026.htm ZHAI Y, ZENG L, XIONG W. Performance analysis of SURF descriptor with different local region partitions[J]. Opt. Precision Eng., 2013, 21(9):2395-2404.(in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-GXJM201309026.htm
[17]	孙浩, 王程, 王润生.局部不变特征综述[J].中国图象图形学报, 2011, 16(2):141-151. doi: 10.11834/jig.20110207 SUN H, WANG CH, WANG R SH. A review of local invariant features[J]. J. Image and Graphics, 2011, 16(2):141-151.(in Chinese) doi: 10.11834/jig.20110207
[18]	聂海涛, 龙科慧, 马军, 等.采用改进尺度不变特征变换在多变背景下实现快速目标识别[J].光学精密工程, 2015, 23(8):2349-2356. http://www.cnki.com.cn/Article/CJFDTOTAL-GXJM201508029.htm NIE H T, LONG K H, MA J, et al.. Fast object recognition under multiple varying background using improved SIFT method[J].Opt. Precision Eng., 2015, 23(8):2349-2356.(in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-GXJM201508029.htm
[19]	MIKOLAJCZYK K, SCHMID C. A performance evaluation of local descriptors[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10):1615-1630. doi: 10.1109/TPAMI.2005.188
[20]	FISCHLER M, BOLLES R. Random Sample Consensus:A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography[M]. Burlington:Morgan Kaufmann Publishers Inc., 1987.