Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26064
Title: A study of supervised classification accuracy in fuzzy topological methods
Authors: Shi, W 
Liu, K
Zhang, H
Keywords: Characteristic posterior probabilities matrix
Eigen-value
Fuzzy topology
Multiple classifier system
Issue Date: 2011
Publisher: Elsevier
Source: International journal of applied earth observation and geoinformation, 2011, v. 13, no. 1, p. 89-99 How to cite?
Journal: International journal of applied earth observation and geoinformation 
Abstract: The multiple classifier system (MCS) is an effective automatic classification method, useful in connection with remote sensing analysis techniques. Combining MSC with induced fuzzy topology enables a decomposition of image classes. This fuzzy topological MCS then provides a new and improved approach to classification. The basic classification methods discussed in this paper include maximum likelihood classification (MLC), minimum distance classification (MIND) and Mahalanobis distance classification (MAH). In this paper, the use of the fuzzy topology techniques in combination with the current classification methods is discussed. The methods included are (1) ordinary single classifier classification methods; (2) fuzzy single classifier classification methods; (3) simple average MCS; (4) fuzzy topological simple average MCS; (5) eigen-value MCS; (6) fuzzy topology and eigen-values MCS. This new experimental approach, involving such combinations for comparing the kappa values and overall accuracies is also discussed. After comparing the kappa values and overall accuracies of these classification methods, the experimental results, demonstrated that (a) methods combining with fuzzy topology concepts produced better classification accuracy than the ordinary methods; (b) the eigen-value MCS method produces better classification accuracy than the non-fuzzy method and (c) the best classifier combination was found to be MLC+MIND+MAH fuzzy eigen-value MCS.
URI: http://hdl.handle.net/10397/26064
ISSN: 1569-8432
EISSN: 1872-826X
DOI: 10.1016/j.jag.2010.07.001
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