Improving the performance of extreme learning machine for hyperspectral image classification

Jiaojiao Li; Qian Du; Wei Li; Yunsong Li

doi:10.1117/12.2178013

21 May 2015 Improving the performance of extreme learning machine for hyperspectral image classification

Jiaojiao Li, Qian Du, Wei Li, Yunsong Li

Proceedings Volume 9501, Satellite Data Compression, Communications, and Processing XI; 950109 (2015) https://doi.org/10.1117/12.2178013
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

Abstract

Extreme learning machine (ELM) and kernel ELM (KELM) can offer comparable performance as the standard powerful classifier―support vector machine (SVM), but with much lower computational cost due to extremely simple training step. However, their performance may be sensitive to several parameters, such as the number of hidden neurons. 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 so as to greatly reduce computational cost. Other parameters, such as the steepness parameter in the sigmodal activation function and regularization parameter in the KELM, are also investigated. The experimental results show that classification performance is sensitive to these parameters; fortunately, simple selections will result in suboptimal performance.

Citation Download Citation

Jiaojiao Li, Qian Du, Wei Li, and Yunsong Li "Improving the performance of extreme learning machine for hyperspectral image classification", Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 950109 (21 May 2015); https://doi.org/10.1117/12.2178013

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available