移动高程平面约束的多视影像可靠匹配方法
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摘要
影像匹配是摄影测量技术从二维航空/航天影像自动获取三维空间信息的关键。传统的航空影像匹配受限制于传感器的立体成像能力,以“单立体”影像匹配为主,容易出现“病态解”。随着新型数字传感器的日益广泛使用,获得覆盖同一地区大重叠度的数字影像变得越来越容易,从而催生了多视影像匹配技术的发展并成为该领域研究的热点。
多视影像匹配具有三大显著优势:一是综合利用多视影像信息可以提高匹配的可靠性;二是可以有效解决多视影像匹配中的相对遮挡问题;三是可以根据需要对多视影像进行选择性匹配。现有的多视影像匹配方法仅考虑其中第一个方面的优势,没有对后两种优势进行针对性的研究,匹配方式也较为固定。本文针对城区影像较严重的建筑物遮挡问题,充分发挥多视影像匹配的优势,提出了移动高程平面约束的多视影像可靠匹配方法。该方法利用物方规则格网划分的平面作为约束,分别采用影像上特征点和物方“地面元”作为匹配基元进行了特征点匹配和密集匹配,并根据匹配基元的投影光束,对多视影像进行选择性匹配,避免遮挡对匹配的影响。该方法满足真正的多视影像匹配的概念,可以处理任意数量的影像( n >2),同时可以处理所有待匹配影像上任意重叠度的影像区域。
本文主要研究内容如下:
⑴影像预处理与特征点提取。采用自适应平滑滤波去噪、Wallis滤波增强方法对影像进行预处理,采用本文的实验影像数据,对几种常用的特征点提取算子(Moravec算子、Foerstner算子、Harris算子)从定位精度、特征点提取数量上进行实验比较,最终采用Foerstner算子对所有影像进行特征点提取;
⑵特征点匹配。建立一种新的多视影像同时匹配的物方约束模型。通过移动平面到不同高程位置,利用平面上的格网单元位置约束不同影像上特征点投影光束范围,根据格网单元内通过特征点投影光束的数目进行分级匹配,同时根据格网单元内通过的特征点投影光束对多视影像进行选择性匹配,该过程避免由于特征点在一些影像上存在遮挡、特征点提取过程中没有被提取出来产生的匹配漏洞,匹配过程中对每一个成功匹配的格网单元进行高程赋值;
⑶密集匹配。将特征点匹配后高程赋值的格网平面看作初始的DSM,并作为物方规则分布的“地面元”的载体。将遮挡检测方法引入到多视影像匹配过程中,提出了基于高度的遮挡检测和物方铅垂线约束相结合的多视影像匹配方法,每个“地面元”在匹配前先进行遮挡检测,根据遮挡检测结果选择未发生遮挡的影像进行匹配,避免遮挡对匹配的影响,在多视影像相对遮挡区域取得理想的匹配结果,同时得到每张影像上的区域遮挡图。
⑷提出参考影像不固定的多视影像匹配策略。针对灰度区域相关匹配过程中参考影像的选取,设计新的参考影像选取方法,即根据地底点成像变形最小原理,选择地底点离待匹配格网单元最近的影像作为参考影像,匹配过程中参考影像不固定。实验证明,同等条件下选择地底点最近的影像作为参考影像可以提高灰度窗口间相关系数的最大值。
基于本文研究提出的多视影像匹配方法,采用某一典型地区同一条航带上的四张UCD数码航空影像,进行了实验分析,并与VirtuoZo系统的结果进行了比较,实验结果证明了本文方法的正确性和有效性。通过对多视影像进行选择性匹配,有效的解决建筑物遮挡的影响,为城区多视影像的可靠匹配提供了一条新的解决途径。
Reliable image matching is an essential and difficult task in digital photogrammetry and computer vision. The traditional image matching is restricted to the imaging abilities of stereo sensors, which based on the matching of“single stereo-pair”, and therefore is a challenging and“ill-posed”problem. Along with the increasing use of new digital sensors, it becomes more and more easier to acquire large overlap digital images covering the same area, the multi-view image matching approach has attracted wide interests in both photogrammetry and computer vision.
Multi-view image matching has three significant advantages: Firstly, it improves the reliability of matching utilizing the comprehensive multi-view image information; Secondly, it effectively solves the relative-occluded problem of images; and thirdly, it can selectively match the multiple images on-demand. However, up to now the existing multi-view image matching methods only employ the first advantage, there is very few research about another two characteristics, and the matching strategy is also relative inflexible. Focusing on the serious occlusion problem in city images, this dissertation proposes a reliable multi-view image matching method based on the moving Z-Plane constraint. Following a constraint plane of grid partitioning in object space, this method adopts the image interest points and the“groundel”(ground element) in object space as the matching primitives for feature matching and dense matching. Simultaneously, it carries on the selective matching for multi-view images depending on the projection rays of matching primitives to avoid the occlusion effect in the matching. This method suffices the conception of true multi-view image matching, which can deal with any number of images, and any degree overlap areas in to-be-matched images.
The main contents of this dissertation are as follows:
⑴Image pre-processing and extraction of interest points. In image pre-processing, this dissertation adopts the adaptive smoothing filter to reduce the image noise and the Wallis filter to enhance the image features. It also compares several extraction operators of interest points (Moravec operator、Forstner operator、Harris operator) on location accuracy and numbers of interest points through experimental analysis, and finially employs the Forstner operator to extract interest points in overall images.
⑵Interest point matching. A new constraint mode in object space for the simultaneously multi-view image matching is introduced. By moving the matching plane to different elevation positions so called Z-Plane, it constraints the range of projection rays from different images based on the positions of grid cells in the Z-Plane , and then a related hierarchical matching according to the number of viewing rays in the grid cells is presented. Simultaneously, it carries on a selective matching depending on the viewing rays in the grid cells, in order to avoid the matching vulnerability caused by occlusions or interest point not be extracted. In the process of matching, it also assigns the evaluation values to successfully matched grid cells.
⑶Dense matching. It takes the valued grid plane after interest point matching as the initial DSM, and as the carrier of regular distributing“groundel”in the object space. Introduces the occlusion detection method into the process of“groundel”matching in the object space. Along with the height-based occlusion detection method and the simultaneously multi-image matching based on vertical line constraint in the object space, it carries on the occlusion detection before the matching of each groundel, and selects the images without occlusions to do dense matching according to the result of occlusion detection, in order to avoid the effect of occlusions and to obtain good matching results in relative-occluded areas, even the occlusion map in every image.
⑷A multi-view image matching strategy based on the unfixed reference image is proposed. Focusing on the problem of reference image selection in the process of matching based on gray area correlation, a new algorithm for reference image selection is designed, which selects the nearest image from the ground nadir point as reference image according to the principle of minimum image distortion in nadir point. The reference image is unfixed in the process of matching. The experimental results validate which selects the nearest image from the ground nadir point as reference image under the same conditions can improve the maximum of cross correlation coefficient in gray window with different images.
Based on the multi-view image matching method proposed in this dissertation, it carries on the matching experiment utilizing four large overlap UCD digital airbrone images in the same strip, and compares with the result derived from the VirtuoZo System. The experiment results validate the correctness and effectiveness of this method proposed in this dissertation. Through the selective matching toward multi-view images, this method effectively solves the effect of building occlusion, and provides a new effective solution for reliable multi-view image matching.
多视影像匹配具有三大显著优势:一是综合利用多视影像信息可以提高匹配的可靠性;二是可以有效解决多视影像匹配中的相对遮挡问题;三是可以根据需要对多视影像进行选择性匹配。现有的多视影像匹配方法仅考虑其中第一个方面的优势,没有对后两种优势进行针对性的研究,匹配方式也较为固定。本文针对城区影像较严重的建筑物遮挡问题,充分发挥多视影像匹配的优势,提出了移动高程平面约束的多视影像可靠匹配方法。该方法利用物方规则格网划分的平面作为约束,分别采用影像上特征点和物方“地面元”作为匹配基元进行了特征点匹配和密集匹配,并根据匹配基元的投影光束,对多视影像进行选择性匹配,避免遮挡对匹配的影响。该方法满足真正的多视影像匹配的概念,可以处理任意数量的影像( n >2),同时可以处理所有待匹配影像上任意重叠度的影像区域。
本文主要研究内容如下:
⑴影像预处理与特征点提取。采用自适应平滑滤波去噪、Wallis滤波增强方法对影像进行预处理,采用本文的实验影像数据,对几种常用的特征点提取算子(Moravec算子、Foerstner算子、Harris算子)从定位精度、特征点提取数量上进行实验比较,最终采用Foerstner算子对所有影像进行特征点提取;
⑵特征点匹配。建立一种新的多视影像同时匹配的物方约束模型。通过移动平面到不同高程位置,利用平面上的格网单元位置约束不同影像上特征点投影光束范围,根据格网单元内通过特征点投影光束的数目进行分级匹配,同时根据格网单元内通过的特征点投影光束对多视影像进行选择性匹配,该过程避免由于特征点在一些影像上存在遮挡、特征点提取过程中没有被提取出来产生的匹配漏洞,匹配过程中对每一个成功匹配的格网单元进行高程赋值;
⑶密集匹配。将特征点匹配后高程赋值的格网平面看作初始的DSM,并作为物方规则分布的“地面元”的载体。将遮挡检测方法引入到多视影像匹配过程中,提出了基于高度的遮挡检测和物方铅垂线约束相结合的多视影像匹配方法,每个“地面元”在匹配前先进行遮挡检测,根据遮挡检测结果选择未发生遮挡的影像进行匹配,避免遮挡对匹配的影响,在多视影像相对遮挡区域取得理想的匹配结果,同时得到每张影像上的区域遮挡图。
⑷提出参考影像不固定的多视影像匹配策略。针对灰度区域相关匹配过程中参考影像的选取,设计新的参考影像选取方法,即根据地底点成像变形最小原理,选择地底点离待匹配格网单元最近的影像作为参考影像,匹配过程中参考影像不固定。实验证明,同等条件下选择地底点最近的影像作为参考影像可以提高灰度窗口间相关系数的最大值。
基于本文研究提出的多视影像匹配方法,采用某一典型地区同一条航带上的四张UCD数码航空影像,进行了实验分析,并与VirtuoZo系统的结果进行了比较,实验结果证明了本文方法的正确性和有效性。通过对多视影像进行选择性匹配,有效的解决建筑物遮挡的影响,为城区多视影像的可靠匹配提供了一条新的解决途径。
Reliable image matching is an essential and difficult task in digital photogrammetry and computer vision. The traditional image matching is restricted to the imaging abilities of stereo sensors, which based on the matching of“single stereo-pair”, and therefore is a challenging and“ill-posed”problem. Along with the increasing use of new digital sensors, it becomes more and more easier to acquire large overlap digital images covering the same area, the multi-view image matching approach has attracted wide interests in both photogrammetry and computer vision.
Multi-view image matching has three significant advantages: Firstly, it improves the reliability of matching utilizing the comprehensive multi-view image information; Secondly, it effectively solves the relative-occluded problem of images; and thirdly, it can selectively match the multiple images on-demand. However, up to now the existing multi-view image matching methods only employ the first advantage, there is very few research about another two characteristics, and the matching strategy is also relative inflexible. Focusing on the serious occlusion problem in city images, this dissertation proposes a reliable multi-view image matching method based on the moving Z-Plane constraint. Following a constraint plane of grid partitioning in object space, this method adopts the image interest points and the“groundel”(ground element) in object space as the matching primitives for feature matching and dense matching. Simultaneously, it carries on the selective matching for multi-view images depending on the projection rays of matching primitives to avoid the occlusion effect in the matching. This method suffices the conception of true multi-view image matching, which can deal with any number of images, and any degree overlap areas in to-be-matched images.
The main contents of this dissertation are as follows:
⑴Image pre-processing and extraction of interest points. In image pre-processing, this dissertation adopts the adaptive smoothing filter to reduce the image noise and the Wallis filter to enhance the image features. It also compares several extraction operators of interest points (Moravec operator、Forstner operator、Harris operator) on location accuracy and numbers of interest points through experimental analysis, and finially employs the Forstner operator to extract interest points in overall images.
⑵Interest point matching. A new constraint mode in object space for the simultaneously multi-view image matching is introduced. By moving the matching plane to different elevation positions so called Z-Plane, it constraints the range of projection rays from different images based on the positions of grid cells in the Z-Plane , and then a related hierarchical matching according to the number of viewing rays in the grid cells is presented. Simultaneously, it carries on a selective matching depending on the viewing rays in the grid cells, in order to avoid the matching vulnerability caused by occlusions or interest point not be extracted. In the process of matching, it also assigns the evaluation values to successfully matched grid cells.
⑶Dense matching. It takes the valued grid plane after interest point matching as the initial DSM, and as the carrier of regular distributing“groundel”in the object space. Introduces the occlusion detection method into the process of“groundel”matching in the object space. Along with the height-based occlusion detection method and the simultaneously multi-image matching based on vertical line constraint in the object space, it carries on the occlusion detection before the matching of each groundel, and selects the images without occlusions to do dense matching according to the result of occlusion detection, in order to avoid the effect of occlusions and to obtain good matching results in relative-occluded areas, even the occlusion map in every image.
⑷A multi-view image matching strategy based on the unfixed reference image is proposed. Focusing on the problem of reference image selection in the process of matching based on gray area correlation, a new algorithm for reference image selection is designed, which selects the nearest image from the ground nadir point as reference image according to the principle of minimum image distortion in nadir point. The reference image is unfixed in the process of matching. The experimental results validate which selects the nearest image from the ground nadir point as reference image under the same conditions can improve the maximum of cross correlation coefficient in gray window with different images.
Based on the multi-view image matching method proposed in this dissertation, it carries on the matching experiment utilizing four large overlap UCD digital airbrone images in the same strip, and compares with the result derived from the VirtuoZo System. The experiment results validate the correctness and effectiveness of this method proposed in this dissertation. Through the selective matching toward multi-view images, this method effectively solves the effect of building occlusion, and provides a new effective solution for reliable multi-view image matching.
引文
IJPRS ISPRS Journal of Photogrammetry and Remote Sensing
IAPRS International Archives of Photogrammetry and Remote Sensing
IJCV International Journal of Computer Vision
GRSL IEEE Geoscience and Remote Sensing Letters
PAMI IEEE Transactions on Pattern Analysis and Machine Intelligence
PaREC Pattern Recognition
CVGIP Computer Vision, Graphics and Image processing
SPIE The Society of Photo-Optical Instrumentation Engineers
ECCV European Conference on Computer Vision
ICFPD Proceedings of the Intercommission Conference on Fast-Processing of photogrammetric Data
IEEE-CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition
WSCG International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision
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IAPRS International Archives of Photogrammetry and Remote Sensing
IJCV International Journal of Computer Vision
GRSL IEEE Geoscience and Remote Sensing Letters
PAMI IEEE Transactions on Pattern Analysis and Machine Intelligence
PaREC Pattern Recognition
CVGIP Computer Vision, Graphics and Image processing
SPIE The Society of Photo-Optical Instrumentation Engineers
ECCV European Conference on Computer Vision
ICFPD Proceedings of the Intercommission Conference on Fast-Processing of photogrammetric Data
IEEE-CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition
WSCG International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision
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