基于混沌杜鹃搜索算法的高光谱影像波段选择

胡梦林; 万幼川; 王明威; 高贤君; 高雄

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基于混沌杜鹃搜索算法的高光谱影像波段选择

更新时间：2026-09-04

- 基于混沌杜鹃搜索算法的高光谱影像波段选择
- Band Selection Based on Chaotic Cuckoo Search Algorithm for Hyperspectral Image
- 微电子学与计算机 2018年35卷第4期页码：124-129
- 作者机构：
  
  1. 武汉大学遥感信息工程学院,湖北,武汉,430079
  2. 长江大学机构地球科学学院,湖北,武汉,430100
  3. 中国冶金地质总局地球物理勘察院,河北,保定,071051
- 作者简介：
- 基金信息：
  
  国家重点专项 2016YFC0600210测绘遥感信息工程国家重点实验室开放基金资助项目 13R04长江大学青年基金资助项目 2016cqn04
- DOI：
  中图分类号：
- 纸质出版：2018
- 稿件说明：
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胡梦林, 万幼川, 王明威, 高贤君, 高雄. 基于混沌杜鹃搜索算法的高光谱影像波段选择[J]. 微电子学与计算机, 2018, 35(4): 124-129.

HU Meng-lin, WAN You-chuan, WANG Ming-wei, et al. Band Selection Based on Chaotic Cuckoo Search Algorithm for Hyperspectral Image[J]. 2018, 35(4): 124-129.
胡梦林, 万幼川, 王明威, 高贤君, 高雄. 基于混沌杜鹃搜索算法的高光谱影像波段选择[J]. 微电子学与计算机, 2018, 35(4): 124-129. DOI：

HU Meng-lin, WAN You-chuan, WANG Ming-wei, et al. Band Selection Based on Chaotic Cuckoo Search Algorithm for Hyperspectral Image[J]. 2018, 35(4): 124-129. DOI：

摘要

针对高光谱遥感影像的波段降维问题

结合杜鹃搜索算法良好的全局寻优能力和混沌映射局部寻优能力强的特点

提出一种基于混沌杜鹃搜索算法的高光谱影像波段选择方法

并对HYDICE影像进行仿真实验.同时进行该算法与遗传算法、粒子群算法和基本杜鹃搜索算法的对比实验

实验结果表明该算法搜索能力更强

最终所选波段子集的分类精度更高.

Abstract

A band selection technique based on chaotic cuckoo search algorithm is proposed in this paper

which combined with cuckoo search algorithm and chaotic map to solve the problem of dimension reduction of hyperspectral image. As a common used evolutionary algorithm

cuckoo search algorithm has the better global optimization ability

and chaotic map can improve the local optimization ability of evolutionary algorithm. Hence

a HYDICE image is utilized to test the efficiency of the proposed method. experimental results demonstrate that the search ability of chaotic cuckoo search algorithm is better than genetic algorithm

particle swarm optimization algorithm

and standard cuckoo search algorithm. Meanwhile

the classification accuracy could be increased by using the selected band subset.

关键词

Keywords

references

Yang X S, Deb S. Cuckoo search via Lévy flights[C]//Nature % Biologically Inspired Computing, 2009. IEEE, 2009: 210-214.

Cao X, Li X, Li Z, et al. Hyperspectral band selection with objective image quality assessment[J]. International Journal of Remote Sensing, 2017, 38(12): 3656-3668. DOI: 3656

Medjahed S A, Saadi T A, Benyettou A, et al. Binary cuckoo search algorithm for band selection in hyperspectral image classification[J]. IAENG International Journal of Computer Science, 2015, 42(3): 183-191. DOI: 183

Arslan O, AKYVREK Ö, KAYA S. A comparative analysis of classification methods for hyperspectral images generated with conventional dimension reduction methods[J]. Turkish Journal of Electrical Engineering % Computer Sciences, 2017, 25(1): 58-72. DOI: 58

Datta A, Ghosh S, Ghosh A. Unsupervised band extraction for hyperspectral images using clustering and kernel principal component analysis[J]. International Journal of Remote Sensing, 2017, 38(3): 850-873. DOI: 850

Xu M, Sun Q, He Z, et al. Band selection for hyperspectral images based on particle swarm optimization and differential evolution algorithms with hybrid encoding[J]. Journal of Computational Methods in Sciences and Engineering, 2016, 16(3): 629-640. DOI: 629

Civicioglu P, Besdok E. A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms[J]. Artificial intelligence review, 2013, 39(4): 1-32. DOI: 1

Mantegna R N. Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes[J]. Physical Review E, 1994, 49(5): 4677. DOI: 4677

吴超, 吴一全. 基于混沌粒子群优化投影寻踪的高光谱图像目标检测[J]. 光学学报, 2011, 31(12): 63-70. DOI: 63

陈志刚, 梁涤青, 邓小鸿, 等. Logistic混沌映射性能分析与改进[J]. 电子与信息学报, 2016, 38(6): 1547-1551. DOI: 1547

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