Initialization of Markov random field clustering of large remote sensing images
Tran, T.N.
Wehrens, R.
Hoekman, D.H.
Buydens, L.M.C.
Inst. for Molecules & Mater., Radboud Univ. Nijmegen, Netherlands;
This paper appears in: Geoscience and Remote Sensing, IEEE Transactions on
Publication Date: Aug 2005
Volume: 43,
Issue: 8
On page(s): 1912- 1919
ISSN: 0196-2892
INSPEC Accession Number: 8508005
Digital Object Identifier: 10.1109/TGRS.2005.848427
Current Version Published: 2005-09-19
Abstract
Markov random field (MRF) clustering, utilizing both spectral and spatial interpixel dependency information, often improves classification accuracy for remote sensing images, such as multichannel polarimetric synthetic aperture radar (SAR) images. However, it is heavily sensitive to initial conditions such as the choice of the number of clusters and their parameters. In this paper, an initialization scheme for MRF clustering approaches is suggested for remote sensing images. The proposed method derives suitable initial cluster parameters from a set of homogeneous regions, and estimates the number of clusters using the pseudolikelihood information criterion (PLIC). The method works best for an image consisting of many large homogeneous regions, such as agricultural crops areas. It is illustrated using a well-known polarimetric SAR image of Flevoland in the Netherlands. The experiment shows a superior performance compared to several other methods, such as fuzzy C-means and iterated conditional modes (ICM) clustering.
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