基于Marr小波改进的SIFT算法的遥感影像配准
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摘要
针对遥感图像配准方法中错误匹配点对过多、配准效率低和其他性能,提出了一种基于小波的遥感图像配准方法。首先,利用尺度空间理论下的Marr小波对参考图像和待配准图像进行特征提取,然后利用欧氏距离对参考图像和待配准图像的特征点进行初配准,再根据随机采样一致法,对初配准结果进行精配准。为了验证方法的有效性,选择无人机实时航拍图像、不同时相变化遥感图像以及遥感不同高度的遥感图像。实验结果表明:该方法与SIFT(Scale Invariant Feature Transform)算法以及其他改进SIFT算法相比可以有效剔除错误匹配点对,提高了配准精度,同时提高配准效率两倍以上。该方法可以应用于不同遥感数据源,能够有效地提高配准精度,降低配准时间。
For the traditional remote sensing image registration method,there are too many pairs of registration error matching,and the efficiency of registration is low.In order to further improve the accuracy and efficiency of remote sensing image registration,a remote sensing image registration method based on wavelet was proposed.Firstly,the feature extraction of the reference image and the image to be registered using the Marr wavelet in scale space theory.Then use Euclidean distance to perform initial registration of the feature points of the reference image and the image to be registered.Again consistent with the random sampling method,the registration results for early registration for fine.Experimental results show that this method can effectively eliminate false matching points compared with SIFT and other improved SIFT algorithms.Improve registration accuracy,while improving the efficiency of more than double registration.Conclusion:for traditional remote sensing image registration methods,registration mismatches have many pairs of points and the efficiency is low.This paper presents an accurate remote sensing image registration method.The experimental results show that this method can effectively improve the accuracy of registration and reduce the time of registration.
For the traditional remote sensing image registration method,there are too many pairs of registration error matching,and the efficiency of registration is low.In order to further improve the accuracy and efficiency of remote sensing image registration,a remote sensing image registration method based on wavelet was proposed.Firstly,the feature extraction of the reference image and the image to be registered using the Marr wavelet in scale space theory.Then use Euclidean distance to perform initial registration of the feature points of the reference image and the image to be registered.Again consistent with the random sampling method,the registration results for early registration for fine.Experimental results show that this method can effectively eliminate false matching points compared with SIFT and other improved SIFT algorithms.Improve registration accuracy,while improving the efficiency of more than double registration.Conclusion:for traditional remote sensing image registration methods,registration mismatches have many pairs of points and the efficiency is low.This paper presents an accurate remote sensing image registration method.The experimental results show that this method can effectively improve the accuracy of registration and reduce the time of registration.
引文
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[23] Wang L,Zhang Y,Feng J.On the Euclidean Distance of Images[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2005,27(8):1334-1339.
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[2] Hou Pengyang,Ji Yan,Gao Feng,et al.Fast Hierarchical Registration Method for Remote Sensing Image based on SIFT[J].Remote Sensing Technology and Application,2014,29(5):873-877.[侯鹏洋,季艳,高峰,等.一种基于SIFT特征的快速逐层遥感图像配准方法[J].遥感技术与应用,2014,29(5):873-877.]
[3] Chen Benqing,Yang Yanming,Zheng Linghong,et al.Automatic Registration of Quad-rotor UAV Imagery based on SIFT and TPS Algorithm[J].Remote Sensing Technology And Application,2013,28(4):604-609.[陈本清,杨燕明,郑凌虹,等.基于SIFT和TPS算法的四旋翼无人机图像自动配准[J].遥感技术与应用,2013,28(4):604-609.]
[4] Zhang Q,Cao Z G,Hu Z W,et al.Joint Image Registration and Fusion for Panchromatic and Multi-spectral Images[J].IEEE Geoscience and Remote Sensing Letters,2015,12(3):467-471.
[5] Li J Y,Hu Q W,Ai M Y.Robust Feature Matching for Remote Sensing Image Registration based on Lq-estimator[J].IEEE Geoscience and Remote Sensing Letters,2016,13(12):1989-1993.
[6] Gu Y J,Ren K,Wang P C,et al.Polynomial Fitting-based Shape Matching Algorithm for Multi-sensors Remote Sensing Images[J].Infrared Physics and Technology,2016,76:386-392.
[7] Yu Xianchuan,Lü Zhonghua,Hu Dan.Review of Remote Sensing Image Registration Techniques[J].Optics and Precision Engineering,2013,21(11):2960-2972.[余先川,吕中华,胡丹.遥感图像配准技术综述[J].光学精密工程,2013,21(11):2960-2972.]
[8] Lowe D G.Distinctive Image Features from Scale-invariant Key Points[J].International Journal of Computer Vision,2004,60(2):91-110.
[9] Bay H,Ess A,Tuytelaars T,et al.Speeded-up Robust Features (SURF)[J].Computer Vision & Image Understanding,2008,110(3):346-359.
[10] Mikolajczyk K,Schmid C.Scale and Affine Invariant Interest Point Detectors[J].International Journal of Computer Vision,2004,60(1):63-86.
[11] Pramote O U,Piamsanga P.Improve Accuracy of Disparitymap for Stereo Images Using SIFT and Weighted Color Model[C]//Proceedings of the 7th International Conference on Knowledge and Smart Technology.Chonburi,Thailand,2015:109-114.
[12] Lei T J,Li L,Kan G Y,Zhang Z B,et al.Automatic Registration of Unmanned Aerialvehicle Remote Sensing Images based on An Improved SIFT Al-Gorithm[C]//Proceedings of SPIE Volume 10033,Eighth International Conference on Digital Image Processing.Chengdu,2016:10033.
[13] Harris C.A Combined Corner and Edge Detector[J].Proc Alvey Vision Conf,1988(3):147-151.
[14] Jin Feng,Feng Dazheng.Image Feature Detection and Registration Algorithm based on Mexican Hat Function[J].Computer Science,2014,41(2):280-284.[靳峰,冯大政.基于Mexican Hat函数的图像特征提取和配准算法[J].计算机科学,2014,41(2):280-284.]
[15] Xu Jiajia,Zhang Ye,Zhang He.Fast Image Registration Algorithm based on Improved Harris-SIFT Descriptor[J].Journal of Electronic Measurement and Instrumentation,2015,29(1):48-54.[许佳佳,张叶,张赫.基于改进Harris-SIFT算子的快速图像配准算法[J].电子测量与仪器学报,2015,29(1):48-54.]
[16] Zhao Hongyang,Zhu Yili.Based on Wavelet Transform and Harris Corner Detection Registration Method of Fuzzy Sets[J].Electronics Optics & Control,2016(5):45-49.[赵泓扬,朱义利.基于小波变换和Harris角点检测的模糊集配准方法[J].电光与控制,2016(5):45-49.]
[17] Marr D,Hildreth E.Theory of Edge Detection[J].Proceedings of the Royal Society of London,1980,207(1167):187-217.
[18] Antoine J,Vandergheynst P,Murenzi R.Two-dimensional Directional Wavelets in Image Processing[J].International Journal of Imaging Systems & Technology,1996,7(3):152-165.
[19] Bhattacharjee S,Kutter M.Compression Tolerant Image Authentication[C]//IEEE International Conference on Image Processing,1998.ICIP 98.Proceedings,1998:435-439.
[20] Manjunath B S,Shekhar C,Chellappa R.A New Approach to Image Feature Detection with Applications[J].Pattern Recognition,1996,29(4):627-640.
[21] Dobbins A,Zucker S W,Cynader M S.Endstopping and Curvature[J].Vision Research,1989,29(10):1371-87.
[22] Ding Nannan.Based on the Feature Points of Image Registration Technology Research[D].Beijing:University of Chinese Academy of Sciences,2012.[丁南南.基于特征点的图像配准技术研究[D].北京:中国科学院研究生院,2012.]
[23] Wang L,Zhang Y,Feng J.On the Euclidean Distance of Images[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2005,27(8):1334-1339.
[24] 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.