多源遥感图像配准技术研究
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摘要
随着遥感技术迅速发展和新型传感器的不断涌现,人们获取遥感图像数据的能力不断提高。在利用这些多源遥感图像进行数据融合、目标变化检测、目标识别等多源协同处理工作之前,必须进行多源图像配准工作,配准精度的高低直接影响到后续应用效果的好坏。为此,本文主要研究了多源遥感图像间的配准技术,作为协同系统中的关键技术,要求配准方法在运算能力和配准精度方面都能够达到较好的效果。
首先,本文对现有的多源图像配准技术进行原理上的分析与介绍。通过对多种配准方法的分类与比较,指出了遥感图像配准的通用技术环节与技术要点。并在研究过程中分析关键技术环节的难点与所面临问题。
其次,本文针对传统多源配准方法在进行控制点对应时运算量大,误配情况多的现状,提出了一种基于位置约束的多源遥感影像配准技术。该方法首先利用人工粗略选取少量控制点对,得到粗略位置映射关系,之后利用位置信息以及分辨率信息建立局部窗函数进行搜索匹配,对两幅图像中提取的Harris角点进行筛选,最终得到的控制点对作为求取配准参数的控制点输入,并利用此方法进行了多组图像的实验来证明方法的通用性。
然后,本文针对传统配准方法需要人工参与,并且仅使用单一特征进行匹配效果差的缺点,提出了一种基于多特征组合的多源遥感图像自动配准技术。这种方法利用了由粗至精的配准思想,结合使用点、线、面特征分别进行粗配准及精细配准两个过程。重点解决了其中少量初始控制点对的匹配和更多控制点对的获取。完成了存在闭合区域的多源遥感图像间的自动配准过程,并实验验证了方法的配准精度。
最后,为了对配准后的遥感图像进行直观的视觉评价,本文介绍了配准后图像间的镶嵌以及融合等简单应用。通过实验,可以很直观的看出配准的效果,完成配准的定性评价。
With the development of both remote sensing technology and the new sensor networks, it is improved to access to remote sensing image data. However, when we use these multi-source remote sensing images as the source of fusion, target change detection, target identification, as well as dealing with multi-source collaborative processing, we must do the multi-source image registration work first. The level of registration accuracy directly affects the quality of user’s application. So this paper examined the multi-source remote sensing image registration techniques as key technologies of a collaborative system. Methods of registration require both computing power and the matching accuracy achieve good results.
First of all, this dissertation researches the existing multi-source image registration techniques and introduces the principles of analysis. Take a variety of methods and comparison in consideration, at the same time notice the characteristics of remote sensing image registration, we proposed the common elements of multi-sensor image registration technology. Meanwhile we study and analyze various technical aspects of the difficulties and problems.
Secondly, this dissertation, in order to improve the traditional methods of multi-source in the area of the control point selection during the operation and reduce the mismatched control points, a location-based constraint of multi-source remote sensing image registration techniques are proposed. This method firstly artificially selected a small number of control points as rough selected points. Then we achieve a rough mapping position. Constraint by the location information, as well as resolution information, we establish a local search window to match the two images’feature points. In this dissertation we use the Harris corner features, and ultimately received corresponding points act as an alignment of the control point as input parameters. And the results show that this method has advantages to the traditional method and can receive a trade off results in computation and accuracy.
Then, in this dissertation, the traditional manual method has the disadvantage of requiring man to participate in, and only using a single feature as the corresponding characteristics which lead a poor performance of registration, This dissertation proposes a registration method based on combination of multi-features including Harris corner points cross-road features and closed regions. This technique is suitable for images having large closed regions. Firstly, we automatic extract the corresponding closed regions, and achieve the coarse mapping parameters as geometrical restriction. Secondly, we extract all the Harris corner points and cross-road features between two images, and introduce both correlation analysis and geometrical restriction as the constraints for features matching. The final matched points are imported as control points for registration. The experiment results show that the method can reduce the possibility of false mapping and can achieve a precision of sub pixel.
Finally, in order to give the result of registration of remote sensing images a visual assessment, this paper introduces the matching between the mosaic image, as well as simple application of image fusion. Through experiments, it is intuitive to show the effect of registration as a complement to the qualitative assessment.
首先,本文对现有的多源图像配准技术进行原理上的分析与介绍。通过对多种配准方法的分类与比较,指出了遥感图像配准的通用技术环节与技术要点。并在研究过程中分析关键技术环节的难点与所面临问题。
其次,本文针对传统多源配准方法在进行控制点对应时运算量大,误配情况多的现状,提出了一种基于位置约束的多源遥感影像配准技术。该方法首先利用人工粗略选取少量控制点对,得到粗略位置映射关系,之后利用位置信息以及分辨率信息建立局部窗函数进行搜索匹配,对两幅图像中提取的Harris角点进行筛选,最终得到的控制点对作为求取配准参数的控制点输入,并利用此方法进行了多组图像的实验来证明方法的通用性。
然后,本文针对传统配准方法需要人工参与,并且仅使用单一特征进行匹配效果差的缺点,提出了一种基于多特征组合的多源遥感图像自动配准技术。这种方法利用了由粗至精的配准思想,结合使用点、线、面特征分别进行粗配准及精细配准两个过程。重点解决了其中少量初始控制点对的匹配和更多控制点对的获取。完成了存在闭合区域的多源遥感图像间的自动配准过程,并实验验证了方法的配准精度。
最后,为了对配准后的遥感图像进行直观的视觉评价,本文介绍了配准后图像间的镶嵌以及融合等简单应用。通过实验,可以很直观的看出配准的效果,完成配准的定性评价。
With the development of both remote sensing technology and the new sensor networks, it is improved to access to remote sensing image data. However, when we use these multi-source remote sensing images as the source of fusion, target change detection, target identification, as well as dealing with multi-source collaborative processing, we must do the multi-source image registration work first. The level of registration accuracy directly affects the quality of user’s application. So this paper examined the multi-source remote sensing image registration techniques as key technologies of a collaborative system. Methods of registration require both computing power and the matching accuracy achieve good results.
First of all, this dissertation researches the existing multi-source image registration techniques and introduces the principles of analysis. Take a variety of methods and comparison in consideration, at the same time notice the characteristics of remote sensing image registration, we proposed the common elements of multi-sensor image registration technology. Meanwhile we study and analyze various technical aspects of the difficulties and problems.
Secondly, this dissertation, in order to improve the traditional methods of multi-source in the area of the control point selection during the operation and reduce the mismatched control points, a location-based constraint of multi-source remote sensing image registration techniques are proposed. This method firstly artificially selected a small number of control points as rough selected points. Then we achieve a rough mapping position. Constraint by the location information, as well as resolution information, we establish a local search window to match the two images’feature points. In this dissertation we use the Harris corner features, and ultimately received corresponding points act as an alignment of the control point as input parameters. And the results show that this method has advantages to the traditional method and can receive a trade off results in computation and accuracy.
Then, in this dissertation, the traditional manual method has the disadvantage of requiring man to participate in, and only using a single feature as the corresponding characteristics which lead a poor performance of registration, This dissertation proposes a registration method based on combination of multi-features including Harris corner points cross-road features and closed regions. This technique is suitable for images having large closed regions. Firstly, we automatic extract the corresponding closed regions, and achieve the coarse mapping parameters as geometrical restriction. Secondly, we extract all the Harris corner points and cross-road features between two images, and introduce both correlation analysis and geometrical restriction as the constraints for features matching. The final matched points are imported as control points for registration. The experiment results show that the method can reduce the possibility of false mapping and can achieve a precision of sub pixel.
Finally, in order to give the result of registration of remote sensing images a visual assessment, this paper introduces the matching between the mosaic image, as well as simple application of image fusion. Through experiments, it is intuitive to show the effect of registration as a complement to the qualitative assessment.
引文
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7 L.G. Brown. A Survey of Image Registration Techniques. ACM Computing Surveys, 1992,24(4): 325~376
8 B.Zitova and J.Flusser. Image Registration Methods: a Survey. Image and Vision Computing, 2003,21: 977~1000
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12 Menet S, Saint-Marc P, Medioni G. B-Snakes: Implementation and Application to Stereo. DARPA Image Understanding Workshop, 1990, 720~726
13 Piella. G. A General Framework for Multiresolution Image Fusion: from Pixels to Regions .Information Fusion, 2003, (4) :259~280
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15潘泉,于昕,程咏梅,张洪才.信息融合理论的基本方法与进展.自动化学报. 2003,(04)
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29 H. Alhichri, M. Kamel. Virtual Circles: a New Set of Features for Fast Image Registration. Pattern Recognition Letters 2003, 24:1181~1190
30 K. Mikolajcayk, T. Tuyelaars, C. Shhmid, A. Zisserman, J. Matas, L. Vangool. A Comparision of Affine Region Detectors. International Journal of ComputerVision, 2005
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36 Tuytelaars, T. and Van Gool, L. Matching. Widely Separated Views based on Affine Invariant Regions. International Journal on Computer Vision 2004,59(1):61~85
37 T. Peli. An Algorithm for Recognition and Localization of Rotated and Scaled Objects. Proceedings of the IEEE 1981,69:483~485
38 A. Taza, C.Y. Suen. Description of Planar Shapes Using Shape Matrices. IEEE Transactions on Systems, Man, and Cybernetics 1989 ,19:1281~1289
39 H. Li, B.S. Manjunath, S.K. Mitra. A Contour-based Approach to Multi-sensor Image Registration, IEEE Transactions on Image Processing 1995,4:320~334
40 J. Flusser, T. Suk. A Moment-based Approach to Registration of Images with Affine Geometric Distortion. IEEE Transactions on Geoscience and Remote Sensing 1994,32:382~387
41 D. Bhattacharya, S. Sinha. Invariance of Stereo Images via Theory of Complex Moments. Pattern Recognition 1997,30:1373~1386
42 K. Rohr, Landmark-Based Image Analysis: Using Geometric and Intensity Models. Computational Imaging and Vision Series, vol. 21, Kluwer Academic Publishers, Dordrecht, 2001
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45 S.M. Smith, J.M. Brady, SUSAN. A New Approach to Low Level Image Processing. International Journal of Computer Vision, 1997,23: 45~78
46 D. Lowe. Distinctive Image Features from Scale-invariant Keypoints. International Journal of Computer Vision, 2004,60(2):91~110
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2 A. Arcese, P. Mengert, E. Trombini. Image Detection through Bipolar Correlation. IEEE Transactions on Information Theory, Sep 1970, 16(5): 534~ 541
3 D.I. Barnea, H.F. Silverman. A class of Algorithms for Fast Digital Image Registration. IEEE Transactions on Computers, Feb. 1972, C(21): 179~186
4 M. Svedlow, C.D.MacGillen, Paul E. Anuta. Image registration: Two New Techniques for Image Matching. Proc 5th Joint Conf. On Artificial Intelligence. Cambridge. Mess, 659~663
5 H.G. Barrow et al. Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching. Proc 5th Joint Conf. On Artificial Intelligence. Cambridge. Mess.,1977:659~663
6 Mikio Takagi, Takamichi Hiyama, Mitsuo Sone and Morio Onoe. Automatic Correcting Method of Geometric Distortion in Meteorological Satellite NOAA Image.电子情报通信学会论文志, 1988, J71-D(S): 883~893
7 L.G. Brown. A Survey of Image Registration Techniques. ACM Computing Surveys, 1992,24(4): 325~376
8 B.Zitova and J.Flusser. Image Registration Methods: a Survey. Image and Vision Computing, 2003,21: 977~1000
9夏明革,何友等.多传感器图像融合应用评述.舰船电子对抗. 2002,25(5):38~44
10覃征,鲍复民等.数字图像融合.西安:西安交通大学出版社, 2004.7
11何友,王国宏等.多传感器信息融合及其应用.北京:电子工业出版社. 2000,11
12 Menet S, Saint-Marc P, Medioni G. B-Snakes: Implementation and Application to Stereo. DARPA Image Understanding Workshop, 1990, 720~726
13 Piella. G. A General Framework for Multiresolution Image Fusion: from Pixels to Regions .Information Fusion, 2003, (4) :259~280
14张易凡.多光谱遥感图像融合技术研究.西北工业大学. 2007
15潘泉,于昕,程咏梅,张洪才.信息融合理论的基本方法与进展.自动化学报. 2003,(04)
16倪国强.多波段图像融合算法研究及其发展.光电子技术与信息. Oct.2002.Vol.14.No.5
17过润秋,李俊峰,.林晓春.基于并行遗传算法的红外图像增强及相关技术,兵工学报. 2004(1)
18 D. Ziou, S. Tabbone. Edge Detection Techniques-an overview. http://citeseer.nj. nec. com/ ziou97edge.html, 1997
19 H. Li, B.S. Manjunath, S.K. Mitra. A Contour-based Approach to Multi-sensor Image Registration. IEEE Transactions on Image Processing 4, 1995: 320~334
20 J.B.A. Maintz, P.A. van den Elsen, M.A. Viergever. Evaluation on Ridge Seeking Operators for Multimodality Medical Image Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 1996,18:353~365
21 A. Goshtasby, G.C. Stockman. Point Pattern Matching using Convex Hull Edges. IEEE Transactions on Systems, Man and Cybernetics 1985,15:631~637
22 H.G. Barrow, J.M. Tenenbaum, R.C. Bolles, H.C. Wolf.. Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching. Proceedings of the Fifth International Joint Conference on Artificial Intelligence, Cambridge, Massachusetts, 1977:659~663
23 G. Borgefors. Hierarchical Chamfer Matching: a Parametric Edge Matching Algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence 1988,10:849~865
24 P.J. Besl, N.D. McKay. A Method for Registration of 3D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992,14:239~254
25 C. Stewart, C.-L. Tsai, and B. Roysam. The Dual-Bootstrap Iterative Closest Point Algorithm with Application to Retinal Image Registration. IEEE Trans. Medical Imaging, 2003,22(11):1379~1394
26 G. Yang, C. Stwart, M. Sofka and C.-L. Tsai. Registration of Challenging Image Pairs: Initialization, Estimation, and Decision, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008 29 :1973~1989
27 N.R. Pal, S.K. Pal. A Review on Image Segmentation Techniques. Pattern Recognition 1993,26:1277~1294
28 A. Goshtasby, G.C. Stockman, C.V. Page. A Region-based Approach to Digital Image Registration with Subpixel Accuracy. IEEE Transactions on Geoscience and Remote Sensing 1986,24:390~399
29 H. Alhichri, M. Kamel. Virtual Circles: a New Set of Features for Fast Image Registration. Pattern Recognition Letters 2003, 24:1181~1190
30 K. Mikolajcayk, T. Tuyelaars, C. Shhmid, A. Zisserman, J. Matas, L. Vangool. A Comparision of Affine Region Detectors. International Journal of ComputerVision, 2005
31 K. Mikolajcayk, C. Shhmid. Scale & Affine Invariant Interest Point Detectors. International Journal of Computer Vision 2004,60(1):63~86
32 K. Mikolajcayk, C. Shhmid. An Affine Invariant Interest Point Detector. Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark
33 J. Matas, O. Chum, M. Urban, T. Pajdla. Robust Wide-baseline Stereo from Maximally Stable Extremal Regions. Proceedings of the British Machine Vision Conference, Cardiff, UK, 2002:384~393
34 J. Matas, O. Chum, M. Urban, T. Pajdla Robust Wide-baseline Stereo from Maximally Stable Extremal Regions, Image and Vision Computing, 2004,22(10):761~767
35 Tuytelaars, T. Van Gool, L. D’haene, L. and Koch, R. Matching of Affinely Invariant Regions for Visual Serving. Int. Conference Robotics and Automation ICRA 99, 1999
36 Tuytelaars, T. and Van Gool, L. Matching. Widely Separated Views based on Affine Invariant Regions. International Journal on Computer Vision 2004,59(1):61~85
37 T. Peli. An Algorithm for Recognition and Localization of Rotated and Scaled Objects. Proceedings of the IEEE 1981,69:483~485
38 A. Taza, C.Y. Suen. Description of Planar Shapes Using Shape Matrices. IEEE Transactions on Systems, Man, and Cybernetics 1989 ,19:1281~1289
39 H. Li, B.S. Manjunath, S.K. Mitra. A Contour-based Approach to Multi-sensor Image Registration, IEEE Transactions on Image Processing 1995,4:320~334
40 J. Flusser, T. Suk. A Moment-based Approach to Registration of Images with Affine Geometric Distortion. IEEE Transactions on Geoscience and Remote Sensing 1994,32:382~387
41 D. Bhattacharya, S. Sinha. Invariance of Stereo Images via Theory of Complex Moments. Pattern Recognition 1997,30:1373~1386
42 K. Rohr, Landmark-Based Image Analysis: Using Geometric and Intensity Models. Computational Imaging and Vision Series, vol. 21, Kluwer Academic Publishers, Dordrecht, 2001
43 W. Forstner, E. Gulch. A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Centers of Circular Features. Proceedings of the ISPRS Workshop on Fast Processing of Photogrammetric Data, Interlaken, Switzerland, 1986:281~305
44 A. Noble. Finding Corners. Image and Vision Computing, 6 1988:121~128.
45 S.M. Smith, J.M. Brady, SUSAN. A New Approach to Low Level Image Processing. International Journal of Computer Vision, 1997,23: 45~78
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