基于尺度不变特征变换的异源图像配准方法  

Scale-Invariant Feature Transform-Based Heterogeneous Image Registration Method

作　　者：刘鹏南 徐冬冬 白春梦 Liu Pengnan;Xu Dongdong;Bai Chunmeng(Shandong Gold Mining(Laixi)Co..Ltd.,Qingdao,Shandong 266000,China;School of Information and Control Engineering,China University of Mining and Technology,Xuzhou,Jiangsu 221000,China)

机构地区：[1]山东黄金矿业(莱西)有限公司,山东青岛266000 [2]中国矿业大学信息与控制工程学院,江苏徐州221000

出　　处：《激光与光电子学进展》2021年第24期164-173,共10页Laser & Optoelectronics Progress

基　　金：国家重点研发计划(2018YFC0808302)。

摘　　要：针对异源图像配准中传感器物理特性差异造成的待匹配特征维度较高、稳定性较弱、配准质量较差等问题,提出一种基于尺度不变特征变换(SIFT)的异源图像配准方法。该方法首先结合相位一致性和改进的SIFT算法获得稳定的特征,然后利用最近邻距离比方法进行初匹配,接着提出了一种联合误差与欧氏距离(JEED)方法进行再匹配,最后采用模式搜索尺度不变特征变换(MS-SIFT)方法优化匹配点对以提高图像配准质量。实验结果表明,相比于现有方法,所提方法能够提取可靠稳定的特征,获得了较高配准质量,同时提高了配准算法的实时性。Aiming at the problems of high dimensionality, weak stability, and poor registration quality of the features to be matched caused by the difference in sensor physical characteristics in heterogeneous image registration, this paper proposes a scale-invariant feature transform(SIFT)-based heterogeneous image registration method. This method combines the phase consistency and improved SIFT algorithm to obtain stable features. Next, it uses the nearest neighbor distance ratio method for initial matching. Then, we propose a joint error and Euclidean distance(JEED) method for rematching. The mode-seeking scale-invariant feature transform(MS-SIFT) method is employed to optimize the matching point pairs to improve the image registration quality. Experimental results show that, compared with the existing methods, the method proposed in this paper can extract reliable and stable features, obtain higher registration quality, and improve the real-time performance of the registration algorithm.

关 键 词：图像处理 异源图像 图像配准 特征点 尺度不变特征变换算法 分层区域 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]