A band selection method of hyperspectral remote sensing based on particle frog leaping algorithm  

作　　者：MU Lin-lin ZHANG Chao-zhu CHI Peng-fei LIU Lian 穆琳琳;张朝柱;池鹏飞;刘潋(College of Information and Communication Engineering, Harbin Engineering University)

机构地区：College of information and Communication Engineering, Harbin Engineering University, Harbin 150001, China

出　　处：《Optoelectronics Letters》2018年第4期316-319,共4页光电子快报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61571149)

摘　　要：Dimensionality reduction is becoming an important problem in hyperspectral image classification. Band selection as an effective dimensionality reduction method has attracted more research interests. In this paper, a band selection method for hyperspectral remote sensing images based on subspace partition and particle frog leaping optimization algorithm is proposed. Three new evolution strategies are designed to form a probabilistic network extension structure to avoid local convergence. At the same time, the information entropy of the selected band subset is used as the weight of inter-class separability, and a new band selection criterion function is constructed. The simulation results show that the proposed algorithm has certain advantages over the existing similar algorithms in terms of classification accuracy and running time.

关 键 词：优化算法 跳跃 青蛙 粒子 遥感 图象分类 减小方法 进化策略 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP751[自动化与计算机技术—控制科学与工程]