Band selection for hyperspectral image classification using extreme learning machine

Jiaojiao Li; Benjamin Kingsdorf; Qian Du

doi:10.1117/12.2263039

5 May 2017 Band selection for hyperspectral image classification using extreme learning machine

Jiaojiao Li, Benjamin Kingsdorf, Qian Du

Proceedings Volume 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII; 101980R (2017) https://doi.org/10.1117/12.2263039
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

Extreme learning machine (ELM) is a feedforward neural network with one hidden layer, which is similar to a multilayer perceptron (MLP). To reduce the complexity in the training process of MLP using the traditional backpropagation algorithm, the weights in ELM between input and hidden layers are random variables. The output layer in the ELM is linear, as in a radial basis function neural network (RBFNN), so the output weights can be easily estimated with a least squares solution. It has been demonstrated in our previous work that the computational cost of ELM is much lower than the standard support vector machine (SVM), and a kernel version of ELM can offer comparable performance as SVM. In our previous work, we also investigate the impact of the number of hidden neurons to the performance of ELM. Basically, more hidden neurons are needed if the number of training samples and data dimensionality are large, which results in a very large matrix inversion problem. To avoid handling such a large matrix, we propose to conduct band selection to reduce data dimensionality (i.e., the number of input neurons), thereby reducing network complexity. Experimental results show that ELM using selected bands can yield similar or even better classification accuracy than using all the original bands.

Conference Presentation

Citation Download Citation

Jiaojiao Li, Benjamin Kingsdorf, and Qian Du "Band selection for hyperspectral image classification using extreme learning machine", Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980R (5 May 2017); https://doi.org/10.1117/12.2263039

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