Authors:
Soumia Boumeddane
1
;
Leila Hamdad
1
;
Sophie Dabo-Niang
2
and
Hamid Haddadou
1
Affiliations:
1
Laboratoire de la Communication dans les Systèmes Informatiques, Ecole nationale Supérieure d’Informatique, BP 68M, 16309, Oued-Smar, Algiers and Algeria
;
2
Laboratoire LEM, Université Lille 3, Lille and France
Keyword(s):
Kernel Density Estimation, Kernel Discriminant Analysis, Spatial Information, Supervised Classification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
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, an
d show that our method is competitive and achieves higher classification accuracy compared to other contextual classification methods.
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