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

Muhammad Ahmad, Adil Khan, Manuel Mazzara, Salvatore Distefano

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.

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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 - Volume 4: VISAPP, ISBN 978-989-758-354-4, pages 75-82. DOI: 10.5220/0007258000750082


in Bibtex Style

@conference{visapp19,
author={Muhammad Ahmad and Adil 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 - 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 - 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