A MRF model-based segmentation approach to classification for multispectral imagery

An unsupervised segmentation approach to classification of multispectral image is suggested here in Markov random field (MRF) frame work. This work generalizes the work of Sarkar et al. (2000) on gray value images for multispectral images and is extended for landuse classification. The essence of th...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2002-05, Vol.40 (5), p.1102-1113
Main Authors: Sarkar, A., Biswas, M.K., Kartikeyan, B., Kumar, V., Majumder, K.L., Pal, D.K.
Format: Article
Language:English
Subjects:
Applied geophysics
Classification
Data analysis
Earth sciences
Earth, ocean, space
Exact sciences and technology
Image segmentation
Internal geophysics
Magnetorheological fluids
Markov random fields
Mathematical models
Maximum likelihood estimation
Multispectral imaging
Optimization
Pixel
Remote sensing
Segmentation
Space technology
Statistical distributions
Studies
Surface layer
Testing
Texture
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Summary:An unsupervised segmentation approach to classification of multispectral image is suggested here in Markov random field (MRF) frame work. This work generalizes the work of Sarkar et al. (2000) on gray value images for multispectral images and is extended for landuse classification. The essence of this approach is based on capturing intrinsic characters of tonal and textural regions of any multispectral image. The approach takes an initially oversegmented image and the original. multispectral image as the input and defines a MRF over region adjacency graph (RAG) of the initially segmented regions. Energy function minimization associated with the MRF is carried out by applying a multivariate statistical test. A cluster validation scheme is outlined after obtaining optimal segmentation. Quantitative evaluation of classification accuracy of test data for three illustrations are shown and compared with conventional maximum likelihood procedure. Comparison of the proposed methodology with a recent work of texture segmentation in the literature has also been provided. The findings of the proposed method are found to be encouraging.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2002.1010897