基于数据同化和粒子群优化算法的遥感影像融合

陈荣元; 张飞艳; 张斌; 秦前清

doi:10.3724/SP.J.1146.2008.01442

基于数据同化和粒子群优化算法的遥感影像融合

doi: 10.3724/SP.J.1146.2008.01442

陈荣元^{①②;张飞艳},
张飞艳,
张斌,
秦前清

基金项目:

国家973计划项目(2006CB701303)，国家863计划项目(2006AA12Z132)，测绘国家重点实验室开放研究基金(遥感数据高性能处理的理论与方法研究)，长沙市软科学基金(K0802190-41)和湖南省教育厅科研基金(08C485，09C567)资助课题

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出版历程
- 收稿日期: 2008-11-03
- 修回日期: 2009-03-23
- 刊出日期: 2009-10-19

Remote Sensing Image Fusion Based on Data Assimilation and Particle Swarm Optimization

摘要

摘要: 针对现有的基于色彩空间变换、基于统计以及基于多尺度分析等融合模型的建立主要依赖于经验，其参数选取存在主观性，基本上不能根据融合图像的后续使用目的进行自适应调整的问题，该文提出了一种基于数据同化和粒子群算法的遥感影像融合框架。此框架根据后续处理对影像各个属性指标值的依赖程度确定各个属性指标的权重，构造由影像各个属性评价指标的加权和所组成的目标函数，再利用粒子群算法优化目标函数从而获取合适的影像。一组实验以熵、平均梯度、标准方差、空间频率和结构相似性等定量指标验证了该框架的有效性。
- 遥感影像融合; 数据同化; 粒子群算法
Abstract: The construction of available fusion models, such as that based on color space transform, on statistical, on multi-scale analysis is mostly based on the experience. These models have the problem that the parameters are subjectively chosen and can not be adjusted according to the follow applications of the fusion image. A image fusion framework of remote sensing is proposed based on data assimilation and particle swarm algorithm. Under this framework, weights of different attributes are determined according to their importance degree to the following process and object function is composed of the property weights, then the particle swarm algorithm is used to optimize the object function to get suitable image. A group experiments with the help of the entropy, mean gradient, standard variance, spatial frequency and structure similarity verify effectiveness of the framework.

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参考文献(1)

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