地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (8): 986-994.doi: 10.3724/SP.J.1047.2015.00986

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萤火虫算法优化的高光谱遥感影像极限学习机分类方法

蔡悦(), 苏红军*(), 李茜楠   

  1. 河海大学地球科学与工程学院,南京 210098
  • 收稿日期:2014-10-13 修回日期:2015-02-25 出版日期:2015-08-10 发布日期:2015-08-05
  • 通讯作者: 苏红军 E-mail:cy_0717@163.com;hjsurs@163.com
  • 作者简介:

    作者简介:蔡 悦(1991-),男,硕士生,研究方向为高光谱遥感分类。E-mail: cy_0717@163.com

  • 基金资助:
    国家自然科学基金项目(41201341);中国科学院数字地球重点实验室开放基金项目(2014LDE003);河海大学中央高校基本科研业务费项目(2014B08514)

An Extreme Learning Machine Optimized by Firefly Algorithm for Hyperspectral Image Classification

CAI Yue(), SU Hongjun*(), LI Qiannan   

  1. School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
  • Received:2014-10-13 Revised:2015-02-25 Online:2015-08-10 Published:2015-08-05
  • Contact: SU Hongjun E-mail:cy_0717@163.com;hjsurs@163.com
  • About author:

    *The author: SHEN Jingwei, E-mail:jingweigis@163.com

摘要:

机器学习方法在高光谱遥感影像分类中广泛应用,本文使用新型的极限学习机(Extreme Learning Machine,ELM)进行高光谱遥感影像分类,针对ELM中正则化参数C和核参数σ,提出以萤火虫算法(Firefly Algorithm,FA)进行优化。首先,采用萤火虫算法进行高光谱遥感影像的波段选择,以便降低维数;然后,利用萤火虫算法以分类精度最大化为准则对ELM的参数组合(C,σ)进行寻优;最后,利用参数优化后的ELM分类器,对3个不同传感器的高光谱遥感影像进行分类。实验中将新型的萤火虫算法与遗传算法(Genetic Algorithm,GA)和粒子群算法(Particle Swarm Optimization,PSO)进行了对比,并将ELM的性能与支持向量机(Support Vector Machine,SVM)方法作对比。结果表明,FA优化方法优于传统的GA和PSO优化方法,ELM方法的效果在训练时间和分类准确率2个方面都优于SVM方法。实验说明,本文提出的方法具有较好的适用性和较优的分类效果。

关键词: 极限学习机, 高光谱遥感, 分类, 参数优化

Abstract:

Machine learning technology has been widely used in remote sensing image classification. Extreme learning machine (ELM) is proposed recently for image classification, but the regularization and kernel parameters (C, σ) of ELM have significant influence on classification performance. In this paper, an ELM classifier with firefly algorithm (FA)-based parameter optimization is proposed for hyperspectral image classification. Firstly, FA algorithm is used for band selection in order to reduce the computational load of hyperspectral image classification. Then, the parameters (C, σ) of ELM are optimized by FA with respect to the classification accuracy. In our experiments, the firefly algorithm is also compared with other parameter optimization algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the support vector machine (SVM)-based classification algorithm is also implemented for comparison purpose. The experiments are conducted on three classical hyperspectral remote sensing data. Results indicate that the performance of ELM method is better than SVM method from the aspects of classification accuracy and running time. Our experiments successfully prove that the proposed algorithm can provide a better performance for hyperspectral image classification.

Key words: ELM, hyperspectral remote sensing, classification, parameter optimization