Special Section on Management and Analytics of Remotely Sensed Big Data

Optimizing extreme learning machine for hyperspectral image classification

[+] Author Affiliations
Jiaojiao Li, Yunsong Li

Xidian University, School of Telecommunication, Xi’an 710071, China

Qian Du

Mississippi State University, Department of Electrical and Computer Engineering, Starkville, Mississippi 39762, United States

Wei Li

Beijing University of Chemical Technology, College of Information Science and Technology, Beijing 100029, China

J. Appl. Remote Sens. 9(1), 097296 (Mar 02, 2015). doi:10.1117/1.JRS.9.097296
History: Received December 30, 2014; Accepted February 6, 2015
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Abstract.  Extreme learning machine (ELM) is of great interest to the machine learning society due to its extremely simple training step. Its performance sensitivity to the number of hidden neurons is studied under the context of hyperspectral remote sensing image classification. An empirical linear relationship between the number of training samples and the number of hidden neurons is proposed. Such a relationship can be easily estimated with two small training sets and extended to large training sets to greatly reduce computational cost. The kernel version of ELM (KELM) is also implemented with the radial basis function kernel, and such a linear relationship is still suitable. The experimental results demonstrated that when the number of hidden neurons is appropriate, the performance of ELM may be slightly lower than the linear SVM, but the performance of KELM can be comparable to the kernel version of SVM (KSVM). The computational cost of ELM and KELM is much lower than that of the linear SVM and KSVM, respectively.

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Citation

Jiaojiao Li ; Qian Du ; Wei Li and Yunsong Li
"Optimizing extreme learning machine for hyperspectral image classification", J. Appl. Remote Sens. 9(1), 097296 (Mar 02, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.097296


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