Study the effect of MRF Model based NC classifier with different distance measures and parameters

Abstract

The accuracy of satellite image classification and the computational complexity is reduced due to the image's noisy pixels. Therefore, spatial contextual information-based classifiers are required to handle the noisy pixels and obtain the neighborhood information. This paper represents Noise clustering (NC) based Markov Random Field (MRF) models (SP, DA (H1, H2, H3, and H4)) that handle the noisy pixels and provide the information. The Smoothing Prior (SP) and Discontinuity Adaptive (DA) models are useful for reducing noise by smoothing the images and showing the boundary of classes, respectively. This study has carried out a comparative study among MRF model-based NC classifiers SP and DA for different distance measures and parameters. MRF models based on NC classifiers were tested for classifying Eucalyptus, Water, Riverine sand, Grassland, Dense Forest, and Wheat classes using the Formosat-2 and Landsat-8 multispectral images of the Haridwar area. The DA (H1) model provides the best overall accuracy (85.09%) for m=1.3, λ=0.2, δ=104,γ=0.8, and Mean Absolute Difference.