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Hybrid Bayesian Classifier for Improved Classification Accuracy

Ramachandra, TV and Kumar, U and Mukhopadhyay, C and Raja, SK (2011) Hybrid Bayesian Classifier for Improved Classification Accuracy. In: IEEE Geoscience and Remote Sensing Letters, 8 (3). pp. 474-477.

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Abstract

The widely used Bayesian classifier is based on the assumption of equal prior probabilities for all the classes. However, inclusion of equal prior probabilities may not guarantee high classification accuracy for the individual classes. Here, we propose a novel technique-Hybrid Bayesian Classifier (HBC)-where the class prior probabilities are determined by unmixing a supplemental low spatial-high spectral resolution multispectral (MS) data that are assigned to every pixel in a high spatial-low spectral resolution MS data in Bayesian classification. This is demonstrated with two separate experiments-first, class abundances are estimated per pixel by unmixing Moderate Resolution Imaging Spectroradiometer data to be used as prior probabilities, while posterior probabilities are determined from the training data obtained from ground. These have been used for classifying the Indian Remote Sensing Satellite LISS-III MS data through Bayesian classifier. In the second experiment, abundances obtained by unmixing Landsat Enhanced Thematic Mapper Plus are used as priors, and posterior probabilities are determined from the ground data to classify IKONOS MS images through Bayesian classifier. The results indicated that HBC systematically exploited the information from two image sources, improving the overall accuracy of LISS-III MS classification by 6% and IKONOS MS classification by 9%. Inclusion of prior probabilities increased the average producer's and user's accuracies by 5.5% and 6.5% in case of LISS-III MS with six classes and 12.5% and 5.4% in IKONOS MS for five classes considered.

Item Type: Journal Article
Additional Information: Copyright 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: Bayesian classifier;prior probability;unmixing.
Department/Centre: Division of Information Sciences > Management Studies
Date Deposited: 02 Feb 2012 06:35
Last Modified: 02 Feb 2012 06:35
URI: http://eprints.iisc.ernet.in/id/eprint/43241

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