【作者】 张春美；

【导师】 张永生； 龚志辉；

【作者基本信息】 解放军信息工程大学 ， 摄影测量与遥感， 2008， 硕士

【摘要】 图像匹配技术是计算机视觉和数字图像处理的重要内容,在目标识别与分析、机器人视觉导航、模式识别、医学影像分析等众多领域有着广泛的应用。成像条件的变化,造成了遥感图像之间的几何变形和灰度差异,这些因素都给匹配带来了困难,为了使图像匹配技术得到更完美的应用,众多学者都致力于图像匹配技术的研究。本文对遥感图像目标匹配问题进行了研究和探讨,深入研究了基于特征点的匹配算法,围绕特征点提取、基于特征点位置关系的匹配方法以及基于特征点特征描述的匹配方法三个方面进行了研究和实验。本文的主要工作和创新点如下:1.分析了特征点匹配的重要性和研究的必要性,详细归纳总结了特征点提取技术和图像匹配技术的分类、研究现状及应用现状,分析了图像匹配技术面临的主要问题。2.重点研究了直接基于图像灰度信息的特征点提取方法,分析了摄影测量领域中几种常用的特征点提取算子和机器视觉领域中的尺度不变特征变换算子(SIFT),并通过实验,对各算子的速度、精度以及适应性等进行了综合评价。3.在对Hausdorff距离和遗传算法分析的基础上,提出了一种改进的Huasdorff距离形式IHD(Improved Hausdorff Distance)和一种用于图像匹配的遗传搜索策略,设计了一种以IHD为匹配测度,遗传搜索为匹配策略的基于特征点位置关系的图像匹配方法。实验证明,在遥感影像之间存在部分变形和明显灰度畸变的情况下,该方法能够较好地实现目标匹配。4.深入研究了SIFT特征描述符的生成方法,提出了利用特征点邻域的圆形区域构造描述符的方法,设计了一种基于改进SIFT特征描述符的特征点匹配方法。实验表明,对于分辨率接近的光学影像或雷达影像,在影像之间存在复杂的几何变形和灰度变化的情况下,该方法仍能取得令人满意的目标匹配结果。更多还原

【Abstract】 The art of image matching is a critical content in computer vision and digital image processing, which is widely used in domains of object recognition and analysis, robots vision navigation, pattern recognition, medical image analysis, and so on. However, geometry deformation and grayscale distortion between remote sensing images caused by differences of imaging conditions are big problems in image matching. In order to get perfect matching results in many applications, numerous researchers are engaged in the study of image matching technique. In this thesis, the investigation and discuss are carried out on object matching of remote sensing images, especially on feature point based image matching algorithms. Most work is focused on feature point detecting, matching based on position relationships of feature points, and matching based on descriptors of feature points. The main work and innovations in the thesis are listed as follows.1. Significance and necessity of feature point detecting and image matching are analyzed. Detailed conclusions and summaries are given on their classifications, current research states as well as application states, and problems of image matching are analyzed.2. Emphasis research is given on feature point detectors which are based on gray information of the image directly. Analysis is made on several point detectors frequently used in photogrammetry and a hot detector of Sale Invariant Feature Transform (SIFT) detector in computer vision. A series of experimental comparisons and performance evaluations of these detectors are done under the criteria of velocity, accuracy, as well as adaptability.3. Based on the investigation of several existing Hausdorff distances and the genetic algorithm, an improved Hausdorff distance (IHD) and an efficacious genetic strategy of image matching are presented. Then, a matching method based on position relationships of feature points is designed, which takes feature points instead of the matched image, IHD as the matching measure, and genetic algorithm as the matching strategy. Experimental results prove that this method can get correct matching results effectively when there are obvious intensity variation and partial image distortion in remote sensing images.4. Based upon the deep research on SIFT descriptor, a new SIFT descriptor creation method is proposed, which takes advantage of a circle region around the feature point to creat the descriptor, and then a matching method based on the improved SIFT descriptor is designed. Experimental results show that, when there are complex geometry distortions and illuminance variations between visible spectral or radar remote sensing images with similar resolutions, the matching method proposed by this thesis can still obtain statisfied object matching results.更多还原