An image matching algorithm combining local and semi-global geometry preservation

ZHENG Meiyan; CHEN Jun; GE Xiaoqing; ZHANG Hong

doi:10.7523/j.ucas.2022.019

Journal of University of Chinese Academy of Sciences >

2024 , Vol. 41 >Issue 1: 107 - 116

DOI: https://doi.org/10.7523/j.ucas.2022.019

Research Articles

An image matching algorithm combining local and semi-global geometry preservation

ZHENG Meiyan ,
CHEN Jun ,
GE Xiaoqing ,
ZHANG Hong

Expand

1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;
2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;
3. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China;
4. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2022-01-13

Revised date: 2022-03-16

Online published: 2022-03-16

Fold

Abstract

Remote sensing image matching is an essential preprocessing step for many remote sensing applications. However, the distortions caused by elevation differences and the complexity of remote sensing image matching severely limit the matching precision of high-resolution remote sensing images. This paper proposes a robust feature matching algorithm suitable for local distortion and high outlier ratio. First, the Delaunay triangulation algorithm is used to impose geometric constraints on the initial matching point set, and the local adjacency relationship of the feature points is obtained. Second, pre-filter is conducted based on the adjacency information. Third, a multi-scale strategy is used to establish the local adjacency consistent model. Finally, a triangle similarity function is defined to achieve matching recovery. The experimental results on high-resolution images show that the average accuracy of our algorithm is 7.69% higher than that of RANSAC, and it is still robust when the outlier ratio is higher than 90%.

Key words： high resolution remote sensing image; image matching; Delaunay triangulation; geometry preservation; multi-scale

Cite this article

ZHENG Meiyan , CHEN Jun , GE Xiaoqing , ZHANG Hong . An image matching algorithm combining local and semi-global geometry preservation[J]. Journal of University of Chinese Academy of Sciences, 2024 , 41(1) : 107 -116 . DOI: 10.7523/j.ucas.2022.019

References

[1] Li J Y, Hu Q W, Ai M Y, et al. Robust feature matching via support-line voting and affine-invariant ratios[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 132: 61-76. DOI:10.1016/j.isprsjprs.2017.08.009.
[2] Ma J Y, Jiang X Y, Fan A X, et al. Image matching from handcrafted to deep features: a survey[J]. International Journal of Computer Vision, 2021, 129(1): 23-79. DOI:10.1007/s11263-020-01359-2.
[3] 贾迪, 朱宁丹, 杨宁华, 等. 图像匹配方法研究综述[J]. 中国图象图形学报, 2019, 24(5): 677-699. DOI：10.11834/jig.180501.
[4] Yan X H, Zhang Y J, Zhang D J, et al. Multimodal image registration using histogram of oriented gradient distance and data-driven grey wolf optimizer[J]. Neurocomputing, 2020, 392: 108-120. DOI:10.1016/j.neucom.2020.01.107.
[5] Le Moigne J, Campbell W J, Cromp R F. An automated parallel image registration technique based on the correlation of wavelet features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(8): 1849-1864. DOI:10.1109/TGRS.2002.802501.
[6] 韩松来, 王钰婕, 王星, 等. 多尺度PCA-HOG遥感异源图像匹配算法[J]. 国防科技大学学报, 2022, 44(1): 146-155. DOI:10.11887/j.cn.202201021.
[7] Ye Y X, Shan J, Hao S Y, et al. A local phase based invariant feature for remote sensing image matching[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 142: 205-221. DOI:10.1016/j.isprsjprs.2018.06.010.
[8] Liu S Y, Jiang J. Registration algorithm based on line-intersection-line for satellite remote sensing images of urban areas[J]. Remote Sensing, 2019, 11(12): 1400. DOI:10.3390/rs11121400.
[9] Sedaghat A, Ebadi H. Remote sensing image matching based on adaptive binning SIFT descriptor[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(10): 5283-5293. DOI:10.1109/TGRS.2015.2420659.
[10] Jiang X Y, Ma J Y, Fan A X, et al. Robust feature matching for remote sensing image registration via linear adaptive filtering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(2): 1577-1591. DOI:10.1109/TGRS.2020.3001089.
[11] Li J Y, Hu Q W, Ai M Y. Robust geometric model estimation based on scaled welsch q-norm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(8): 5908-5921. DOI:10.1109/TGRS.2020.2972982.
[12] Liu Y Z, Li Y P, Dai L Y, et al. Robust feature matching via advanced neighborhood topology consensus[J]. Neuro-computing, 2021, 421: 273-284. DOI:10.1016/j.neucom.2020.09.047.
[13] Ma J Y, Zhao J, Tian J W, et al. Robust point matching via vector field consensus[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 2014, 23(4): 1706-1721. DOI:10.1109/TIP.2014.2307478.
[14] Zhang K, Li X Z, Zhang J X. A robust point-matching algorithm for remote sensing image registration[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(2): 469-473. DOI:10.1109/LGRS.2013.2267771.
[15] Zhao J, Ma J Y, Tian J W, et al. A robust method for vector field learning with application to mismatch removing[C]//CVPR 2011. June 20-25, 2011, Colorado Springs, CO, USA. IEEE, 2011: 2977-2984. DOI:10.1109/CVPR.2011.5995336.
[16] Huber P J. Robust statistics[M]. New York: John Wiley & Sons, Inc., 1981.
[17] Hubert M, Debruyne M. Minimum covariance determinant[J]. WIREs Computational Statistics, 2010, 2(1): 36-43. DOI:10.1002/wics.61.
[18] Li J Y, Hu Q W, Ai M Y. Robust feature matching for remote sensing image registration based on L_q-estimator[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1989-1993. DOI:10.1109/LGRS.2016.2620147.
[19] Ma J Y, Zhou H B, Zhao J, et al. Robust feature matching for remote sensing image registration via locally linear transforming[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(12): 6469-6481. DOI:10.1109/TGRS.2015.2441954.
[20] Lin W Y, Wang F, Cheng M M, et al. CODE: coherence based decision boundaries for feature correspondence[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(1): 34-47. DOI:10.1109/TPAMI.2017.2652468.
[21] 李云红, 钟晓妮, 王延年, 等. 基于函数拟合的SIFT误匹配点剔除算法[J]. 激光与红外, 2018, 48(9): 1174-1180. DOI：10.3969/j.issn.1001-5078.2018.09.020.
[22] Ma J Y, Zhao J, Zhou Y, et al. Mismatch removal via coherent spatial mapping[C]//2012 19th IEEE International Conference on Image Processing. September 30-October 3, 2012, Orlando, FL, USA. IEEE, 2012: 1-4. DOI:10.1109/ICIP.2012.6466780.
[23] Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381-395. DOI：10.1145/358669.358692.
[24] Chum O, Matas J. Matching with PROSAC-progressive sample consensus[C]//2005 IEEE Computer Society 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.
[25] Ni K, Jin H L, Dellaert F. GroupSAC: efficient consensus in the presence of groupings[C]//2009 IEEE 12th International Conference on Computer Vision. September 29-October 2, 2009, Kyoto. IEEE, 2009: 2193-2200. DOI:10.1109/ICCV.2009.5459241.
[26] Li R, Sun J Q, Gong D, et al. ARSAC: efficient model estimation via adaptively ranked sample consensus[J]. Neurocomputing, 2019, 328: 88-96. DOI:10.1016/j.neucom.2018.02.103.
[27] Raguram R, Chum O, Pollefeys M, et al. USAC: a universal framework for random sample consensus[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 2022-2038. DOI:10.1109/TPAMI.2012.257.
[28] 甄艳, 刘学军, 王美珍. 一种改进RANSAC的基础矩阵估计方法[J]. 测绘通报, 2014(4): 39-43. DOI:10.13474/j.cnki.11-2246.2014.0120.
[29] Chen H Y, Lin Y, Chen B Y. Robust feature matching with alternate Hough and inverted Hough transforms[C]//2013 IEEE Conference on Computer Vision and Pattern Recognition. June 23-28, 2013, Portland, OR, USA. IEEE, 2013: 2762-2769. DOI:10.1109/CVPR.2013.356.
[30] 谢亮, 陈姝, 张钧, 等. 利用Hough变换的匹配对提纯[J]. 中国图象图形学报, 2015, 20(8): 1017-1025. DOI:10.11834/jig.20150804.
[31] Sedaghat A, Mokhtarzade M, Ebadi H. Uniform robust scale-invariant feature matching for optical remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4516-4527. DOI:10.1109/TGRS.2011.2144607.
[32] Chin T J, Suter D. The maximum consensus problem: recent algorithmic advances[J]. Synthesis Lectures on Computer Vision, 2017, 7(2): 1-194. DOI:10.2200/s00757ed1v01y201702cov011.
[33] Cho M, Lee J, Lee K M. Reweighted random walks for graph matching[M]//Computer Vision-ECCV 2010. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010: 492-505. DOI:10.1007/978-3-642-15555-0_36.
[34] Cho M, Lee K M. Mode-seeking on graphs via random walks[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition. June 16-21, 2012, Providence, RI, USA. IEEE, 2012: 606-613. DOI:10.1109/CVPR.2012.62 47727.
[35] Xiao G B, Luo H, Zeng K, et al. Robust feature matching for remote sensing image registration via guided hyperplane fitting[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-14. DOI:10.1109/TGRS.2020.3041270.
[36] Aguilar W, Frauel Y, Escolano F, et al. A robust graph transformation matching for non-rigid registration[J]. Image and Vision Computing, 2009, 27(7): 897-910. DOI:10.1016/j.imavis.2008.05.004.
[37] Ma J Y, Zhao J, Jiang J J, et al. Locality preserving matching[J]. International Journal of Computer Vision, 2019, 127(5): 512-531. DOI:10.1007/s11263-018-1117-z.
[38] Ma J Y, Jiang J J, Zhou H B, et al. Guided locality preserving feature matching for remote sensing image registration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(8): 4435-4447. DOI:10.1109/TGRS.2018.2820040.
[39] 姜三, 江万寿. Delaunay三角网约束下的影像稳健匹配方法[J]. 测绘学报, 2020, 49(3): 322-333. DOI：10.11947/j.AGCS.2020.20190089.
[40] 毛克乐. 改进SURF和Delaunay三角网在图像匹配中应用[J]. 沈阳工业大学学报, 2021, 43(4): 432-438. DOI：10.7688/j.issn.1000-1646.2021.04.13.
[41] 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.00000 29664.99615.94.
[42] Vedaldi A, Fulkerson B. Vlfeat: an open and portable library of computer vision algorithms[C]//MM’10: Proceedings of the 18th ACM international conference on Multimedia. October 25-29, 2010, Firenze, Italy. ACM, 2010: 1469-1472. DOI:10.1145/1873951.1874249.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References