Fusion of image classifications using Bayesian techniques with Markov random fields

This study investigates whether combining several different image classifications together with an a priori image model of the expected spatial distribution of the classes can produce a better classification. A maximum likelihood classifier and the cascade-correlation neural network architecture are...

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Bibliographic Details
Published in:International journal of remote sensing 1999-01, Vol.20 (10), p.1987-2002
Main Authors: Warrender, Christina E., Augusteijn, Marijke F.
Format: Article
Language:English
Subjects:
Applied geophysics
Earth sciences
Earth, ocean, space
Exact sciences and technology
Internal geophysics
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Summary:This study investigates whether combining several different image classifications together with an a priori image model of the expected spatial distribution of the classes can produce a better classification. A maximum likelihood classifier and the cascade-correlation neural network architecture are used to generate various classification maps for satellite image data by varying the input features and network parameter settings. A likelihood for each pixel's class label is derived from the source classifications and combined with a Markov random field spatial image model to produce the final image classification. The method is applied to a ground cover type study based on Landsat Thematic Mapper (TM) imagery. It was found that a carefully selected combination could significantly improve individual classification results.
ISSN:0143-1161
1366-5901
DOI:10.1080/014311699212308