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A kernel discriminant analysis for spatially dependent data

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Abstract

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.

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Acknowledgements

The authors would like to thank Dr Pedram Ghamisi for kindly providing us the standard training and test sets for Indian Pines and Pavia University datasets.

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Authors and Affiliations

  1. Laboratoire de la Communication dans les Systèmes Informatiques, Ecole nationale Supérieure d’Informatique, BP 68M, 16309, Oued-Smar, Algiers, Algeria

    Soumia Boumeddane, Leila Hamdad & Hamid Haddadou

  2. Univ. Lille, CNRS, UMR 8524 - Laboratoire Paul Painlevé, 59000, Lille, France

    Sophie Dabo-Niang

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  1. Soumia Boumeddane
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  2. Leila Hamdad
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  4. Sophie Dabo-Niang
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Correspondence to Soumia Boumeddane.

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Boumeddane, S., Hamdad, L., Haddadou, H. et al. A kernel discriminant analysis for spatially dependent data. Distrib Parallel Databases 39, 583–606 (2021). https://doi.org/10.1007/s10619-020-07309-8

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