Please wait a minute...
img

官方微信

遥感技术与应用  2019, Vol. 34 Issue (6): 1296-1304    DOI: 10.11873/j.issn.1004-0323.2019.6.1296
数据与图像处理     
一种融合最小二乘和相位相关的粗匹配纠正算法
宋文平1(),张斌2,3(),牛常领4,5,郭亮亮1
1.同济大学测绘与地理信息学院,上海 20092
2.贵州省第三测绘院, 贵州 贵阳 550004
3.贵州师范大学地理与环境科学学院, 贵州 贵阳 550001
4.青岛市西海岸基础地理信息中心,山东 青岛 266035
5.青岛市勘察测绘研究院,山东 青岛,266000
An Integrated Matching Correction Algorithm based on Least Squares and Phase Correlation
Wenping Song1(),Bin Zhang2,3(),Changling Niu4,5,Liangliang Guo1
1.College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
2.The Third Surveying and Mapping Institute Guizhou Province, Guiyang 550004, China
3.School of Geography and Environment Science, Guizhou Normal University, Guiyang 550001, China
4.Qingdao West Coast Geomatics Center, Qingdao 266035, China
5.Qingdao Geotechnical Investigation Surveying Research Insititute, Qingdao 266000, China
 全文: PDF(4348 KB)   HTML
摘要:

针对影像粗匹配后的同名点对匹配不准的问题,提出了一种序列窗口下融合最小二乘和相位相关的影像匹配纠正算法。该方法是在已有粗匹配的基础上进行,首先使用最小二乘对粗匹配结果进行处理,并选取一系列大小变化的影像窗口分别进行最小二乘匹配,得出匹配结果并计算每个匹配窗口的相关系数值。然后选用大小序列变化的影像窗口分别进行相位相关匹配,同时记录匹配结果并计算每个匹配窗口的相关系数值,以相关系数的大小作为衡量同名匹配正确与否的指标,选择相关系数最大的窗口下的匹配结果作为最终的结果。以山西山地区域的无人机影像作为实验基础数据,得出序列窗口下融合最小二乘和相位相关的影像匹配纠正算法能够显著提高粗匹配结果的精度,达到精匹配的目的。

关键词: 影像匹配最小二乘匹配相关系数相位相关匹配    
Abstract:

In order to solve the problem of in-corrected results for preliminary matching. This study proposed an image matching correction algorithm which combines least squares and phase correlation. The method is based on the existing preliminary matching. First, the preliminary matching results are processed using least squares, and a series of image windows in different size are selected to carry out the least squares matching, such that the matching results are obtained and the correlation coefficients of each matching window are calculated. Then, the phase correlation matching are carried out using image windows with different sizes so as to record the matching results. At the same time, the correlation coefficients are calculated. In order to verify the algorithm, the Unmanned Aerial Vehicle (UAV) images in the Shanxi mountain area are selected ad the experimental data. After experiments, it is concluded that the image matching correction algorithm combined least square and phase correlation in window series can significantly improve the precision of preliminary matching results so as to achieve the matching refinements.

Key words: Image Matching    Least Square image Matching    Correlation Coefficients    Phase Correlation Matching
收稿日期: 2018-07-11 出版日期: 2020-03-23
ZTFLH:  P407.1  
基金资助: 国家自然科学基金项目(41361091);贵州省水利科技项目(KT201825)
通讯作者: 张斌     E-mail: m15891747328@163.com;zhangbinbleeding0@163.com
作者简介: 宋文平(1990-),男,山东临沂人,博士研究生,主要从事卫星摄影测量研究。E?mail: m15891747328@163.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
宋文平
张斌
牛常领
郭亮亮

引用本文:

宋文平,张斌,牛常领,郭亮亮. 一种融合最小二乘和相位相关的粗匹配纠正算法[J]. 遥感技术与应用, 2019, 34(6): 1296-1304.

Wenping Song,Bin Zhang,Changling Niu,Liangliang Guo. An Integrated Matching Correction Algorithm based on Least Squares and Phase Correlation. Remote Sensing Technology and Application, 2019, 34(6): 1296-1304.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.6.1296        http://www.rsta.ac.cn/CN/Y2019/V34/I6/1296

图1  SIFT匹配算法流程 [ 22]
图2  最小二乘影像匹配算法流程 [ 24]
图3  相位相关匹配算法流程
图4  本文序列窗口下的匹配纠正算法流程
图5  实验数据
图6  sift粗匹配后的同名点对分布
图7  相关系数介于0.8~1.0的同名点对频数分布
方案误匹配剔除方法序列窗口
A单独最小二乘
B单独相位相关
C单独最小二乘
D单独相位相关
E融合最小二乘和相位相关
表1  主要实验方案
图8  在不使用序列窗口下单独使用最小二乘
图9  在不使用序列窗口下单独使用相位相关
图10  在序列窗口下单独使用最小二乘
图11  在序列窗口下单独使用相位相关
图12  本文序列窗口下融合最小二乘和相位相关
实验方案ρ∈[0.8,0.9]ρ∈[0.9,1.0]
基础数据15358
方案A12487
方案B14467
方案C71140
方案D12487
方案E65146
表2  主要实验结果
实验方案ρ∈[0.8,0.9]ρ∈[0.9,1.0]
基础数据313363
方案A167509
方案B264412
方案C114562
方案D212464
方案E69607
表3  验证实验主要实验结果
1 Brown L G. A Survey of Image Registration Techniques[J]. ACM Computing Surveys, 1992, 24( 4): 325- 376.
2 Yan Li, Hu Xiu Bin, Chen Changjun, et al. Gradient Consistency Operator for Multimodal Image Registration[J]. Geomatics and Information Science of Wuhan University, 2013, 38( 8): 969- 972.
2 闫利, 胡修兵, 陈长军, 等. 多模态图像配准的梯度一致性算子[J]. 武汉大学学报: 信息科学版, 2013, 38( 8): 969- 972.
3 Qin R, Tian J, Reinartz P. 3D Change Detection-approaches and Applications[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 122: 41- 56.
4 Zhang J. Multi-Source Remote Sensing Data Fusion: Status And Trends[J]. International Journal of Image and Data Fusion, 2010, 1( 1): 5- 24.
5 Zhang L, Gruen A. Multi-image Matching for DSM Generation from IKONOS Imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2006, 60( 3): 195- 211.
6 Remondino F, Spera M G, Nocerino E, et al. State of The Art in High Density Image Matching[J]. The Photogrammetric Record, 2014, 29( 146): 144- 166.
7 Wurm M, Taubenböck H, Schardt M, et al. Object-based Image Information Fusion Using Multisensor Earth Observation Data over Urban Areas[J]. International Journal of Image and Data Fusion, 2011, 2( 2): 121- 147.
8 Zhang Chao. Image Registration Algorithm Based on Local Features and Its Application[D]. Beijing: Beijing Insititute of Technology, 2015.
8 张超. 基于局部特征的图像配准算法及应用研究[D]. 北京: 北京理工大学, 2015.
9 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.
10 Moigne J L, Netanyahu N S, Eastman R D. Image Registration for Remote Sensing[M]. London: Cambridge University Press, 2011.
11 Dawn S, Saxena V, Sharma B. Remote Sensing Image Registration Techniques: A Survey[C]∥ International Conference on Image and Signal Processing. Springer, Berlin, Heidelberg, 2010: 103- 112.
12 Gruen A. Development and Status of Image Matching in Photogrammetry[J]. The Photogrammetric Record, 2012, 27( 137): 36- 57.
13 Zavorin I, Le Moigne J. Use of Multiresolution Wavelet Feature Pyramids for Automatic Registration of Multisensor Imagery[J]. IEEE Transactions on Image Processing, 2005, 14( 6): 770- 782.
14 Ackermann F. Digital Image Correlation: Performance and Potential Application in Photogrammetry[J]. The Photogrammetric Record, 1984, 11( 64): 429- 439.
15 Zhang Zuxun, Zhang Jianqing. High Precision Automatic Registration of Remote Sensing Image[J]. Journal of Wuhan University of Surveying and Mapping Technology, 1998, 23( 4): 320- 323.
15 张祖勋, 张剑清. 遥感影像的高精度自动配准[J]. 武汉测绘科技大学学报, 1998, 23( 4): 320- 323.
16 Gruen, Armin. Adaptive Least Squares Correlation: a Powerful Image Matching Technique[J]. South African Journal of Photogrammetry, Remote Sensing and Cartography, 1985, 14( 3): 175- 187.
17 Axelsson, Owe. "A Generalized Conjugate Gradient, Least Square Method[J]. Numerische Mathematik, 1987, 51( 2): 209- 227.
18 Kuglin C. The Phase Correlation Image Alignment Method[C]∥Proceedings of the International Conference on Cybernetics and Society, 23–25 September 1975.
19 Tzimiropoulos G, Argyriou V, Zafeiriou S, et al. Robust Fft-Based Scale-invariant Image Registration with Image Gradients[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32( 10): 1899- 1906.
20 Ojansivu V, Heikkila J. Image Registration Using Blur-Invariant Phase Correlation[J]. IEEE Signal Processing Letters, 2007, 14( 7): 449- 452.
21 Ng P C, Henikoff S. SIFT: Predicting Amino Acid Changes That Affect Protein Function[J]. Nucleic Acids Research, 2003, 31( 13): 3812- 3814.
22 Zeng Fanyong, Gu Aihui, Chen Haifeng, et al. The Realization and Research of Correlation Coefficient and Least Square Image Matching Algorithm[J]. Journal of Water Resources and Architectural Engineering, 2015, 13( 6): 203- 208.
22 曾凡永, 顾爱辉, 陈海峰, 等. 相关系数和最小二乘影像匹配算法的实现与研究[J]. 水利与建筑工程学报, 2015, 13( 6): 203- 208.
23 Tong X H, Ye Z, Xu Y, et al. A Novel Sub-pixel Phase Correlation Method Using Singular Value Decomposition and Unified Random Sample Consensus[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53( 8): 4143- 4156.
24 Nagashima S, Aoki T, Higuchi T, et al. A Sub-pixel Image Matching Technique Using Phase-only Correlation[C]∥International Symposium on Intelligent Signal Processing and Communications, ISPACS'06, 2006.
25 Foroosh, Hassan, Zerubia Josiane B., Berthod Marc. Extension of Phase Correlation to Subpixel Registration[J]. IEEE Transactions on Image Processing, 2002, 11( 3): 188- 200.
26 Sharif M, Khan S, Saba T, et al. Improved Video Stabilization Using Sift-Log Polar Technique for Unmanned Aerial Vehicles[C]∥ 2019 International Conference on Computer and Information Sciences (ICCIS), IEEE, 2019: 1- 7.
27 Wan X, Wang C, Li S. The Extension of Phase Correlation To Image Perspective Distortions based on Particle Swarm Optimization[J]. Sensors, 2019, 19( 14): 3117- 3120.
28 Zhu, G J, Zhou H B, Tao Y W. Weighted Least Square Method for Epipolar Rectification in Semi-Calibrated Image[C]∥ Mippr 2017: Pattern Recognition and Computer Vision. Vol. 10609. International Society for Optics and Photonics, 2018.
29 Filippov A. Method for The Fpga-based Long Range Multi-View Stereo With Differential Image Rectification: U.S. Patent Application 16 /132, 343[P]. 2019-3-28.
[1] 廖小超,胡坚,李传荣,吴克,薛博,李群智. 基于迭代式动态规划的影像密集匹配[J]. 遥感技术与应用, 2015, 30(1): 92-98.
[2] 张彦丽,李丑荣,王秀琴,张鹏吉. 基于WorldView-2制备大野口流域高分辨率DEM及精度分析[J]. 遥感技术与应用, 2013, 28(3): 431-436.
[3] 周 刚, 许德伟, 杨燕明, 刘贞文, 傅世锋. 基于IDL语言与控制点影像数据库的图像自动配准技术研究[J]. 遥感技术与应用, 2010, 25(5): 627-631.
[4] 张 露, 郭华东, 李新武. 利用POLSAR数据探索极化相关系数在居民地提取中的作用[J]. 遥感技术与应用, 2010, 25(4): 474-479.
[5] 刘子龙,董臻,蔡斌,孙造宇. 星载InSAR非相干图像对精确配准[J]. 遥感技术与应用, 2008, 23(4): 446-450.
[6] 韩松涛,向茂生. 一种基于干涉成像几何的水体类地表干涉处理方法[J]. 遥感技术与应用, 2007, 22(1): 105-108.
[7] 杨茂龙, 夏德深. 跑道毁伤识别中的变化检测研究[J]. 遥感技术与应用, 2005, 20(5): 474-477.