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Proceedings Paper

Two approaches to the sample set condensation: experiments with remote sensing images
Author(s): Adam Jozwik; Sebastiano Bruno Serpico; Fabio Roli
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Paper Abstract

The k-NN rules and their modifications offer usually very good performance. The main disadvantage of the k-NN rules is the necessity of keeping the reference set (i.e. training set) in the computer memory. Numerous algorithms for the reference set reduction have been already created. They concern the 1-NN rule and are based on the consistency idea. The 1-NN rule operating with a consistent reduced set classifies correctly, by virtue of consistency, all objects from the original reference set. Quite different approach, based on partitioning of the reference set into some subsets, was proposed earlier by the present authors. The gravity centers of the subsets form the reduced reference set. The paper compares the effectiveness of the two approaches mentioned above. Ten experiments with real data concerning remote sensing data are presented to show the superiority of the approach based on the reference set partitioning idea.

Paper Details

Date Published: 17 December 1996
PDF: 5 pages
Proc. SPIE 2955, Image and Signal Processing for Remote Sensing III, (17 December 1996); doi: 10.1117/12.262876
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. 2955:
Image and Signal Processing for Remote Sensing III
Jacky Desachy, Editor(s)

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