Authors:
Soumia Boumeddane
1
;
Leila Hamdad
1
;
Hamid Haddadou
1
and
Sophie Dabo-Niang
2
Affiliations:
1
Laboratoire de la Communication dans les Systèmes Informatiques, Ecole Nationale Supérieure d’Informatique, BP 68M, 16309, Oued-Smar, Algiers, Algeria
;
2
Univ. Lille, CNRS, UMR 8524, Laboratoire Paul Painlevé, F-59000 Lille, France
Keyword(s):
Spatial Kernel Discriminant Analysis, Feature Extraction, Principle Component Analysis, Particle Swarm Optimization, Hyperspectral Image Classification.
Abstract:
Spatial Kernel Discriminant Analysis is a powerful tool for the classification of spatially dependent data. It allows taking into consideration the spatial autocorrelation of data based on a spatial kernel density estimator. The performance of SKDA is highly influenced by the choice of the smoothing parameters, also known as bandwidths. Moreover, computing a kernel density estimate is computationally intensive for high-dimensional datasets. In this paper, we consider the bandwidth selection as an optimization problem, that we resolve using Particle Swarm Optimization algorithm. In addition, we investigate the use of Principle Component Analysis as a feature extraction technique to reduce computational complexity and overcome curse of dimensionality drawback. We examined the performance of our model on Hyperspectral image classification. Experiments have given promising results on a commonly used dataset.