Open AccessComparison of the classification methods for the images modeled by Gaussian random fields
Author(s) -
Lijana Stabingienė,
Giedrius Stabingis,
Kęstutis Dučinskas
Publication year - 2011
Publication title -
lietuvos matematikos rinkinys
Language(s) - English
Resource type - Journals
eISSN - 2335-898X
pISSN - 0132-2818
DOI - 10.15388/lmr.2011.mt04
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , contextual image classification , random forest , gaussian , naive bayes classifier , image (mathematics) , discriminant , range (aeronautics) , bayes' theorem , bayesian probability , support vector machine , physics , materials science , composite material , quantum mechanics
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.