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Proceedings Paper

Network of modified 1-NN and fuzzy k-NN classifiers in application to remote sensing image recognition
Author(s): Adam Jozwik; Sebastiano Bruno Serpico; Fabio Roli
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Paper Abstract

A parallel network of modified 1-NN classifiers and fuzzy k-NN classifiers is proposed. All the component classifiers decide between two classes only. They operate as follows. For each class i a certain area Ai is constructed. If the classified point lies outside of each area Ai, then the classification is refused. When it belongs only to one of the areas Ai, then the classification is being performed by 1-NN rule. Points that lie in an overlapping area of some areas Ai, are classified by the fuzzy k-NN rule with hard (nonfuzzy) output. Two feature selection sessions are recommended. One to minimise the size of overlapping areas, another to minimise an error rate for the fuzzy k-NN rule. The aim of this work is to create a classifier that is nearly as fast as 1-NN rule and which performance is as good as that for the fuzzy k-NN rule. The effectiveness of the proposed approach was verified on a real data set containing 5 classes, 15 features and 2440 objects.

Paper Details

Date Published: 30 December 1994
PDF: 7 pages
Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); doi: 10.1117/12.196756
Show Author Affiliations
Adam Jozwik, Institute of Biocybernetics and Biomedical Engineering (Poland)
Sebastiano Bruno Serpico, Univ. of Genoa (Italy)
Fabio Roli, Univ. of Genoa (Italy)


Published in SPIE Proceedings Vol. 2315:
Image and Signal Processing for Remote Sensing
Jacky Desachy, Editor(s)

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