测绘科学技术学报2011，Vol.28Issue(2)：121-124,4.DOI:10.3969/j.issn.1673-6338.2011.02.011
基于粒子群优化的模糊特征自适应选择方法
Adaptive Fuzzy Feature Selection Based on Particle Swarm Optimization
摘要
Abstract
Fuzzy feature selection affects fuzzy classification results; however, it is difficult to select effective fuzzy features from large numbers of fuzzy features in fuzzy classification. Particle swarm optimization (PSO) is a new evolutionary computing technique that is based on swarm intelligence. 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. Discrete binary PSO was used to get the optimal fuzzy feature combination adaptively, and the fuzzy feature selection method based on PSO was applied to aerial and satellite remote sensing image fuzzy classification. The experimental results showed that the proposed method was effective.关键词
粒子群优化算法/模糊特征选择/模糊分类/自适应/遥感Key words
particle swarm optimization/ fuzzy feature selection/ fuzzy classification/ adaptive/ remote sensing分类
天文与地球科学引用本文复制引用
李林宜,李德仁..基于粒子群优化的模糊特征自适应选择方法[J].测绘科学技术学报,2011,28(2):121-124,4.基金项目
国家自然科学基金资助项目(41001255) （41001255）
地理空间信息工程国家测绘局重点实验室经费资助项目(201021) （201021）
中央高校基本科研业务费专项资金资助项目(4082007). （4082007）
