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Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery
- Source :
-
Pattern Recognition Letters . Jan2002, Vol. 23 Issue 1-3, p183. 8p. - Publication Year :
- 2002
-
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, crisp and fuzzy k-nearest neighbor (kNN) classifiers are compared. Composite fuzzy classifier architectures are investigated. Experiments are conducted on airborne visible/infrared imaging spectrometer (AVIRIS) data, and the results are evaluated in the paper. [Copyright &y& Elsevier]
- Subjects :
- *ALGORITHMS
*GENETIC programming
*IMAGING systems
*INFRARED technology
Subjects
Details
- Language :
- English
- ISSN :
- 01678655
- Volume :
- 23
- Issue :
- 1-3
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition Letters
- Publication Type :
- Academic Journal
- Accession number :
- 12981683
- Full Text :
- https://doi.org/10.1016/S0167-8655(01)00118-0