Genetic feature selection combined with fuzzy kNN for hyperspectral satellite imagery

Author

Yu, Shixin ; De Backer, Steve ; Scheunders, Paul

Author_Institution

Dept. of Phys., Antwerp Univ., Belgium

Volume

2

fYear

2000

fDate

2000

Firstpage

702

Abstract

For high-dimensional data, the appropriate selection of features has a significant effect on the cost and accuracy of an automated classifier. In this paper, a feature selection technique using genetic algorithms is applied, for classification the fuzzy approach is suggested. And the hard and fuzzy kNN classifications are compared. Experiments are conducted on AVIRIS data, and the results are evaluated in the paper

Keywords

genetic algorithms; geophysical signal processing; geophysical techniques; geophysics computing; image classification; multidimensional signal processing; remote sensing; terrain mapping; AVIRIS; automated classifier; fuzzy approach; fuzzy kNN; genetic algorithm; genetic feature selection; geophysical measurement technique; high-dimensional data; hyperspectral satellite imagery; image classification; land surface; multidimensional signal processing; multispectral remote sensing; optical imaging; terrain mapping; Biological cells; Costs; Electronic mail; Error analysis; Genetic algorithms; Hyperspectral imaging; Hyperspectral sensors; Pattern recognition; Physics; Satellites;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-6359-0

Type

conf

DOI

10.1109/IGARSS.2000.861676

Filename

861676