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.

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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

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KEYWORDS

Neurons

Hyperspectral imaging

Image classification

Neural networks

Statistical analysis

Sensors

Computer engineering

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