[Hyperspectral remote sensing image classification based on radical basis function neural network].
Guang Pu Xue Yu Guang Pu Fen Xi
; 28(9): 2009-13, 2008 Sep.
Article
em Zh
| MEDLINE
| ID: mdl-19093550
ABSTRACT
Based on the radial basis function neural network (RBFNN) theory and the specialty of hyperspectral remote sensing data, the effective feature extraction model was designed, and those extracted features were connected to the input layer of RBFNN, finally the classifier based on radial basis function neural network was constructed. The hyperspectral image with 64 bands of OMIS II made by Chinese was experimented, and the case study area was zhongguancun in Beijing. Minimum noise fraction (MNF) was conducted, and the former 20 components were extracted for further processing. The original data (20 dimension) of extraction by MNF, the texture transformation data (20 dimension) extracted from the former 20 components after MNF, and the principal component analysis data (20 dimension) of extraction were combined to 60 dimension. For classification by RBFNN, the sizes of training samples were less than 6.13% of the whole image. That classifier has a simple structure and fast convergence capacity, and can be easily trained. The classification precision of radial basis function neural network classifier is up to 69.27% in contrast with the 51.20% of back propagation neural network (BPNN) and 40. 88% of traditional minimum distance classification (MDC), so RBFNN classifier performs better than the other three classifiers. It proves that RBFNN is of validity in hyperspectral remote sensing classification.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
Zh
Revista:
Guang Pu Xue Yu Guang Pu Fen Xi
Ano de publicação:
2008
Tipo de documento:
Article
País de afiliação:
China