A kernel discriminant analysis for spatially ...
Document type :
Article dans une revue scientifique: Article original
Title :
A kernel discriminant analysis for spatially dependent data
Author(s) :
Boumeddane, Soumia [Auteur]
Laboratoire de la Communication dans les Systèmes Informatiques [ESI] [LCSI]
Hamdad, Leila [Auteur]
Laboratoire de la Communication dans les Systèmes Informatiques [ESI] [LCSI]
Haddadou, Hamid [Auteur]
Laboratoire de la Communication dans les Systèmes Informatiques [ESI] [LCSI]
Dabo-Niang, Sophie [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Laboratoire de la Communication dans les Systèmes Informatiques [ESI] [LCSI]
Hamdad, Leila [Auteur]
Laboratoire de la Communication dans les Systèmes Informatiques [ESI] [LCSI]
Haddadou, Hamid [Auteur]
Laboratoire de la Communication dans les Systèmes Informatiques [ESI] [LCSI]
Dabo-Niang, Sophie [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Journal title :
Distributed and Parallel Databases
Pages :
583-606
Publisher :
Springer
Publication date :
2020-09-01
ISSN :
0926-8782
English keyword(s) :
Kernel density estimation
Kernel discriminant analysis
Spatial autocorrelation
Supervised classification
Hyperspectral images
Kernel discriminant analysis
Spatial autocorrelation
Supervised classification
Hyperspectral images
HAL domain(s) :
Mathématiques [math]/Statistiques [math.ST]
English abstract : [en]
We propose a novel supervised classification algorithm for spatially dependent data, built as an extension of kernel discriminant analysis, that we named Spatial Kernel Discriminant Analysis (SKDA). Our algorithm is based ...
Show more >We propose a novel supervised classification algorithm for spatially dependent data, built as an extension of kernel discriminant analysis, that we named Spatial Kernel Discriminant Analysis (SKDA). Our algorithm is based on a kernel estimate of the spatial probability density function, which integrates a second kernel to take into account spatial dependency of data. In fact, classical data mining algorithms assume that data samples are independent and identically distributed. However, this assumption is not verified when dealing with spatial data characterized by spatial autocorrelation phenomenon. To make an accurate analysis, it is necessary to exploit this rich source of information and to capture this property. We have applied our algorithm to a relevant domain, which consist of the classification of remotely sensed hyperspectral images. In order to assess the efficiency of our proposed method, we conducted experiments on two remotely sensed images datasets (Indian Pines and Pavia University) with different characteristics and scenarios. The experimental results show that our method is competitive and achieves higher classification accuracy compared to other contextual classification methods.Show less >
Show more >We propose a novel supervised classification algorithm for spatially dependent data, built as an extension of kernel discriminant analysis, that we named Spatial Kernel Discriminant Analysis (SKDA). Our algorithm is based on a kernel estimate of the spatial probability density function, which integrates a second kernel to take into account spatial dependency of data. In fact, classical data mining algorithms assume that data samples are independent and identically distributed. However, this assumption is not verified when dealing with spatial data characterized by spatial autocorrelation phenomenon. To make an accurate analysis, it is necessary to exploit this rich source of information and to capture this property. We have applied our algorithm to a relevant domain, which consist of the classification of remotely sensed hyperspectral images. In order to assess the efficiency of our proposed method, we conducted experiments on two remotely sensed images datasets (Indian Pines and Pavia University) with different characteristics and scenarios. The experimental results show that our method is competitive and achieves higher classification accuracy compared to other contextual classification methods.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :