16阶归一化互信息和改进PSO算法的快速图像匹配  

Fast image matching by using mutual information with 16 histogram bins and improved particle swarm optimization algorithm

作　　者：安如[1] 王慧麟[2] 王盈[3] 陈春烨[1] 张琴[1] 徐晓峰[1] 

机构地区：[1]河海大学地球科学与工程学院,南京210098 [2]南京大学地理与海洋科学学院,南京210093 [3]南京大学建筑与城市规划学院,南京210093

出　　处：《吉林大学学报（工学版）》2013年第S1期357-364,共8页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(41271361;40771137);"863"国家高科技研究发展计划项目(2008AA12Z106)

摘　　要：对不同灰度阶互信息的匹配性能进行了分析;以16阶归一化互信息为相似度评价函数,通过增大重新初始化粒子的数量和改进收敛机制,提出一种基于互信息和改进自组织分层粒子群优化算法(IHPSO)的快速图像匹配方法。以不同传感器,不同时间拍摄的同一地区遥感图像为实验数据,分别使用遍历互信息算法以及多种改进PSO算法进行实验,表明该方法有较好的匹配性能,能满足图像快速匹配的需求,如飞行制导、定位和运动追踪。Image matching performance of normalized mutual information with different histogram bins was analyzed and discussed.Taking normalized mutual information with 16 histogram bins as similarity criteria,a fast image matching method was proposed based on an improved self-organizing hierarchical particle swarm optimizer with time-varying Acceleration Coefficients(IHPSO) through increasing the population of particles reinitialized and improving convergence criterion.Taking remotely sensed imageries captured by different sensors at different time as testing data,the algorithm with the exhaustive search method based on mutual information,Standard PSO and some improved PSO were compared.It is proved that the algorithm suggested has better matching performance and can be applied to the areas of fast image matching,e.g.aero craft navigation,positioning and movement tracking.

关 键 词：图像匹配 互信息 粒子群优化 改进自组织分层PSO 导航定位 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]