Condensed version of the k-NN rule for remote sensing images classification

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. In the present paper a method is proposed to reduce the size of the reference set without decreasing the classification quality. Ten different experiments with very large real data sets were performed to check the effectiveness of the new approach. Each experiment involved 5 classes, 15 features, 2440 objects in the training set and 6399 objects in the testing set. The obtained results show that the decision rule based on the condensed reference set can offer even better classification quality than the one derived from the original data set.

Trovami (UniCASearch)
Titolo: Condensed version of the k-NN rule for remote sensing images classification
Autori: 
ROLI, FABIO
mostra contributor esterni 
Data di pubblicazione: 1995
Rivista: 
PROCEEDINGS OF SPIE, THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING  
Handle: http://hdl.handle.net/11584/180747
ISBN: 0819419435
0819419435
Tipologia:4.1 Contributo in Atti di convegno

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