Multi-layer Extreme Learning Machine-based Autoencoder for Hyperspectral Image Classification

Muhammad Ahmad, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano

2019

Abstract

Hyperspectral imaging (HSI) has attracted the formidable interest of the scientific community and has been applied to an increasing number of real-life applications to automatically extract the meaningful information from the corresponding high dimensional datasets. However, traditional autoencoders (AE) and restricted Boltzmann machines are computationally expensive and do not perform well due to the Hughes phenomenon which is observed in HSI since the ratio of the labeled training pixels on the number of bands is usually quite small. To overcome such problems, this paper exploits a multi-layer extreme learning machine-based autoencoder (MLELM-AE) for HSI classification. MLELM-AE learns feature representations by adopting a singular value decomposition and is used as basic building block for learning machine-based autoencoder (MLELM-AE). MLELM-AE method not only maintains the fast speed of traditional ELM but also greatly improves the performance of HSI classification. The experimental results demonstrate the effectiveness of MLELM-AE on several well-known HSI dataset.

Paper Citation

in Harvard Style

Ahmad M., Khan A., Mazzara M. and Distefano S. (2019). Multi-layer Extreme Learning Machine-based Autoencoder for Hyperspectral Image Classification. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 75-82. DOI: 10.5220/0007258000750082

in Bibtex Style

@conference{visapp19,
author={Muhammad Ahmad and Adil Mehmood Khan and Manuel Mazzara and Salvatore Distefano},
title={Multi-layer Extreme Learning Machine-based Autoencoder for Hyperspectral Image Classification},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP},
year={2019},
pages={75-82},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007258000750082},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP
TI - Multi-layer Extreme Learning Machine-based Autoencoder for Hyperspectral Image Classification
SN - 978-989-758-354-4
AU - Ahmad M.
AU - Khan A.
AU - Mazzara M.
AU - Distefano S.
PY - 2019
SP - 75
EP - 82
DO - 10.5220/0007258000750082
PB - SciTePress