Comparison of the classification methods for the images modeled by Gaussian random fields
Articles
Lijana Stabingienė
Klaipeda University
Giedrius Stabingis
Klaipeda University
Kęstutis Dučinskas
Klaipeda University
Published 2011-12-15
https://doi.org/10.15388/LMR.2011.mt04
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Keywords

image classification
Gaussian random fields
supervised classification
Bayes discriminant function
unsupervised classification
grey level co-occurrence matrix

How to Cite

Stabingienė L., Stabingis G. and Dučinskas K. (2011) “Comparison of the classification methods for the images modeled by Gaussian random fields”, Lietuvos matematikos rinkinys, 52(proc. LMS), pp. 200–204. doi: 10.15388/LMR.2011.mt04.

Abstract

In image classification often occur such situations, when images in some level are corrupted by additive noise. Such noise in image classification can be modeled by Gaussian random fields (GRF). In image classification supervised and unsupervised methods are used. In this paper we compare our proposed supervised classification methods based on plugin Bayes discriminant functions (PBDF) (see [6] and [11]) with unsupervised classification method based on grey level co-occurrence matrix (GLCM) (see e.g. [8] and [1]). The remotely sensed image is used for classification (USGS Earth Explorer). Also GRF with different spatial correlation range are generated and added to the original remotely sensed image. Such situation can naturally occur during forest fire, when smoke covers some territory. These images are used for classification accuracy examination.

 

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