Spatial Kernel Discriminant Analysis: Applied for Hyperspectral Image Classification

Soumia Boumeddane, Leila Hamdad, Sophie Dabo-Niang, Hamid Haddadou

2019

Abstract

Classical data mining models relying upon the assumption that observations are independent, are not suitable for spatial data, since they fail to capture the spatial autocorrelation. In this paper, we propose a new supervised classification algorithm which takes into account the spatial dependency of data, named Spatial Kernel Discriminant Analysis (SKDA). We present a non-parametric classifier based on a kernel estimate of the spatial probability density function which combines two kernels: one controls the observed values while the other controls the spatial locations of observations. We applied our algorithm for hyperspectral image (HSI) classification, a challenging task due to the high dimensionality of data and the limited number of training samples. Using our algorithm, the spatial and spectral information of each pixel are jointly used to achieve the classification. To evaluate the efficiency of the proposed method, experiments on real remotely sensed images are conducted, and show that our method is competitive and achieves higher classification accuracy compared to other contextual classification methods.

Paper Citation

in Harvard Style

Boumeddane S., Hamdad L., Dabo-Niang S. and Haddadou H. (2019). Spatial Kernel Discriminant Analysis: Applied for Hyperspectral Image Classification.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 184-191. DOI: 10.5220/0007372401840191

in Bibtex Style

@conference{icaart19,
author={Soumia Boumeddane and Leila Hamdad and Sophie Dabo-Niang and Hamid Haddadou},
title={Spatial Kernel Discriminant Analysis: Applied for Hyperspectral Image Classification},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={184-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007372401840191},
isbn={978-989-758-350-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Spatial Kernel Discriminant Analysis: Applied for Hyperspectral Image Classification
SN - 978-989-758-350-6
AU - Boumeddane S.
AU - Hamdad L.
AU - Dabo-Niang S.
AU - Haddadou H.
PY - 2019
SP - 184
EP - 191
DO - 10.5220/0007372401840191