粒子群优化算法在遥感影像增强中的应用  

Research on Particle Swarm Optimization in Remote Sensing Image Enhancement

作　　者：李林宜[1] 李德仁[2] 

机构地区：[1]武汉大学遥感信息工程学院,湖北武汉430079 [2]武汉大学测绘遥感信息工程国家重点实验室,湖北武汉430079

出　　处：《测绘科学技术学报》2010年第2期116-119,共4页Journal of Geomatics Science and Technology

基　　金：国家自然科学基金资助项目(40523005);武汉大学自主科研资助项目(4082007)

摘　　要：遥感影像的复杂性给影像增强处理带来了困难。非完全Beta函数增强方法具有理想的增强效果,但是,其参数的合理选取是算法的关键与难点。粒子群优化算法(PSO)是基于鸟群群体智能的新型进化计算技术,具有自适应、自组织等智能特性,具有强大的寻找最优解的能力。这里将PSO用于Beta函数参数的自适应选取,实现了基于PSO的非完全Beta函数增强方法,并通过航空和卫星遥感影像的增强实验,验证了该方法的有效性。Due to the complexity of remote sensing images, remote sensing image enhancement becomes a difficult task. Although the incomplete Beta function enhancement method has good enhancement effects, Beta function parameter selection is the key and difficult problem. Particle swarm optimization （PSO） is a new evolutionary computing technique that is based on swarm intelligence of bird flocks. Because of its intelligent properties such as adaptation and self-organizing, PSO has the strong ability to search for the optimal solutions for optimization problems. PSO was used to get the optimal Beta function parameters adaptively. And incomplete Beta function enhancement method based on PSO was applied to aerial and satellite remote sensing image enhancement. The experimental results showed that the proposed method is effective.

关 键 词：粒子群优化算法 遥感 影像增强 自适应 参数选取 

分 类 号：P237[天文地球—摄影测量与遥感]