Temporal updating scheme for probabilistic neural network with application to satellite cloud classification - further results

Author

Azimi-Sadjadi, Mahmood R. ; Gao, Wenfeng ; Haar, Thomas H Vonder ; Reinke, Donald

Author_Institution

Dept. of Electr. Eng., Colorado State Univ., Fort Collins, CO, USA

Volume

12

Issue

5

fYear

2001

fDate

9/1/2001 12:00:00 AM

Firstpage

1196

Lastpage

1203

Abstract

A novel temporal updating approach for probabilistic neural network classifiers was developed by Tian et al. (2000) to account for temporal changes of spectral and temperature features of clouds in the visible and infrared GOES 8 (Geostationary Operational Environmental Satellite) imagery data. In this paper, a new method referred to as moving singular value decomposition (MSVD) is introduced to improve the classification rate of the boundary blocks or blocks containing cloud types with non-uniform texture. The MSVD method is then incorporated into the temporal updating scheme and its effectiveness is demonstrated on several sequences of GOES 8 cloud imagery data. These results indicate that the incorporation of the new MSVD improves the overall performance of the temporal updating process by almost 10%

Keywords

clouds; feature extraction; geophysics computing; image texture; maximum likelihood detection; neural nets; pattern classification; remote sensing; GOES 8 imagery data; Geostationary Operational Environmental Satellite; cloud classification; image texture; maximum likelihood detection; probabilistic neural network; singular value decomposition; spectral features; temperature features; temporal updating; Clouds; Feature extraction; Focusing; Frequency; Infrared imaging; Infrared spectra; Neural networks; Satellites; Singular value decomposition; Temperature distribution;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.950147

Filename

950147