Hyperspectral image classification by AdaBoost weighted composite kernel extreme learning machines

被引:52
作者
Li, Lu [1 ,2 ,3 ]
Wang, Chengyi [2 ]
Li, Wei [1 ]
Chen, Jingbo [2 ]
机构
[1] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China
[2] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Datun Rd North 20A, Beijing 100101, Peoples R China
[3] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
基金
中国国家自然科学基金;
关键词
Hyperspectral image classification; Extreme learning machine; Composite Kernel; AdaBoost; MULTINOMIAL LOGISTIC-REGRESSION;
D O I
10.1016/j.neucom.2017.09.004
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Extreme learning machine (ELM) is an efficient learning algorithm for multi-classification and regression. However, original ELM doesn't consider the weight of each sample in training-set, which may cause the accuracy decreasing especially in imbalanced datasets. Even if each training sample is assigned with an extra weight, the problem on how to determinate the weight adaptively still remains. Inspiration by AdaBoost algorithm, we embed the weighted ELM algorithm in AdaBoost framework. In the meanwhile, we incorporate spatial and spectral information in composite kernel for each sample, which has a good performance in hyperspectral image (HSI) classification. By combining composite kernel methods and Adaboost framework with weighted ELM, a novel algorithm, namely AdaBoost composite kernel extreme learning machines denoted as AdaBoost-WCKELM is proposed. Experimental results demonstrate that the proposed method outperforms current state-of-the-art algorithms and derives a good improvement in HSI classification accuracy. (c) 2017 Published by Elsevier B.V.
引用
收藏
页码:1725 / 1733
页数:9
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