【作者】 刘朝霞；

【导师】 安居白；

【作者基本信息】 大连海事大学 ， 计算机应用技术， 2011， 博士

【摘要】 图像配准是航空遥感领域中重要的图像处理技术,但仍存在着诸多待解决的问题。首先由于航空遥感图像具有动态变化、亮度变化、几何变化大、相似特征多、重叠区域小等特点使得在图像配准过程中存在很多干扰特征。此时,仅靠改善特征提取方法很难进一步提高配准精度。其次由于遥感图像数据量较大,对配准速度要求较高。鉴于上述原因,本文针对不同的问题对航空遥感图像配准算法进行了系统深入的研究,重点研究了特征描述和匹配方法,提出四种图像配准算法,取得了如下创新性成果：1.由于海上目标不断的变化并且没有固定的形状,并且ICP算法在配准含干扰点多的点集时存在很多误匹配。为了准确配准海上动态变化目标图像,提出了基于矩不变量和改进的ICP的图像配准算法。该算法包括两步：粗配准和精配准。在粗配准过程中,通过矩不变量的相似性和空间相对距离去除一些明显不匹配的干扰点。为精配准提供一对好的初始点集。在精配准过程中,使用了解决分派问题的方法对ICP进行了改进,以去除重复匹配和格外点,使算法很快达到收敛。最终得到两个准确的匹配点集和变换矩阵。实验测试证明,该算法有效地提高了配准精度。2.在重叠区域比较小或者含相似特征的的两幅图像中,确定两个形状完全相同的区域是十分困难的。为了更好的解决此问题,本文提出了基于三角形区域的图像配准算法。首先确定三角形区域,然后构造相对矩仿射不变量描述符对其进行仿射不变描述,最终使用遗传算法进行全局匹配找到两个最相似的图。实验表明,该区域仿射不变量描述符能够有效地提高特征的区分能力,该算法能够很好地配准重叠区域低且局部特征相似的图像。3.当光照不一致或目标自身温度发生变化时,获取的图像的亮度可能会发生一定的变化。而区域描述符MSA对非线性亮度变化比较敏感,因此本文提出了一种基于亮度仿射不变量IIMSA的图像配准算法。该算法将多尺度自动卷积算法MSA和多尺度视觉理论算法MSR结合,构造了一个亮度和仿射不变区域描述符IIMSA。基于该描述符,提出了一种相应的全局匹配策略以去除格外点。实验结果表明,该算法能够有效地用于亮度变化不均匀图像的配准。4.为了满足实时在线检测的需要,进一步提高配准算法的效率和精度,本文提出了一种基于图像结构信息的简单健壮的RSOC特征匹配算法。该算法考虑了局部结构和全局信息,首先定义了一个基于邻接顺序的仿射不变量描述符；然后将特征点匹配转化为一个优化问题,并提出一个新的图匹配方法以解决该问题。在图匹配过程中,设计了一个过滤策略。该策略集成双向空间顺序约束和两个决策标准；最终得到结构一致且全局误差最小的两个匹配点集。比较结果表明该算法高效、精确且稳定。更多还原

【Abstract】 Image registration is a very important technique in remote sensing and still has many unsolved problems. Firstly, the ambiguity caused by dynamical objects, illumination change, large geometric transformation, similar patterns and low overlapping area between images can not be solved just by improving feature detection method. Accurate point matching is a critical and challenging process in feature-based image registration, especially for images with a monotonous background. Secondly, as there are mass data to be processed in remote sensing, the image registration algorithms are expected to be effective. To solve these problems, feature descriptor and feature matching are explored. Four improved algorithms are proposed as follows.1. Due to sea targets having no fixed shape and the mismatches in the matching result of Iterative Closest Point (ICP), an image registration algorithm using invariants-based similarity and improved ICP is proposed for registering images with dynamical objects. There are two stages in this algorithm:coarse registration and fine registration. Invariants-based similarity and relative spatial distance are applied to coarse registration. Then an improved ICP algorithm is used for registering images accurately by combining the ICP and a method of solving assignment problem to deal with mismatches. Compared with traditional ICP and NCC, the accuracy of the proposed algorithm is highly improved.2. It is difficult to find two identical regions in the images with low overlapping area and similar patterns. To tackle this problem, an image registration algorithm based on triangle regions is proposed. In this algorithm, relative moment affine invariants are used to evaluate the similarity of two triangle regions, then a new global feature matching method based on Genetic Algorithm is proposed to match the feature points accurately. Experimental results show that the algorithm works well to register images with low overlapping area and similar patterns.3. The intensity of an image pair may be different when they are taken with nonuniform lighting conditions or change in temperature, and MultiScale Autoconvolution (MSA) has difficulty in registering images with nonuniform lighting conditions, so an image registration algorithm based on an illumination and affine invariant is proposed. In this algorithm, an illumination and affine invariant called Illumination Invariant MultiScale Autoconvolution (IIMSA) is proposed to describe triangle regions and evaluate their similarity. IIMSA is the combination of MSA and MultiScale Retinex (MSR). Based on IIMSA, outliers are removed by a global matching strategy. Experiment results demonstrate that the algorithm is suitable for registering images with big illuminant changes.4. The efficiency and precision of traditional image registration algorithm can’t meet the need of some system which requires high real-time, so a simple and robust feature point matching algorithm, called Restricted Spatial Order Constraints (RSOC), is proposed to improve the efficiency and accuracy. In RSOC, both local structure and global information are considered. Based on adjacent spatial order, an affine invariant descriptor is defined and point matching is formulated as an optimization problem. A graph matching method is used to solve it and yields two matched graphs with minimum global transformation error. In order to eliminate dubious matches, a filtering strategy is designed, which integrates two-way spatial order constraints and two decision criteria restrictions. Numerous experiments show that RSOC obtains the highest precision and stability.更多还原