Network of modified 1-NN and fuzzy k-NN classifiers in application to remote sensing image recognition

Adam Jozwik; Sebastiano Bruno Serpico; Fabio Roli

doi:10.1117/12.196756

30 December 1994 Network of modified 1-NN and fuzzy k-NN classifiers in application to remote sensing image recognition

Adam Jozwik, Sebastiano Bruno Serpico, Fabio Roli

Proceedings Volume 2315, Image and Signal Processing for Remote Sensing; (1994) https://doi.org/10.1117/12.196756
Event: Satellite Remote Sensing, 1994, Rome, Italy

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 A_i, 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.

Citation Download Citation

Adam Jozwik, Sebastiano Bruno Serpico, and Fabio Roli "Network of modified 1-NN and fuzzy k-NN classifiers in application to remote sensing image recognition", Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); https://doi.org/10.1117/12.196756

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