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

IEEE Transactions on Neural Networks

Volumn 12, Issue 5, 2001, Pages 1196-1203

  Azimi Sadjadi, Mahmood R ^a   Gao, Wenfeng ^a   Vonder Haar, Thomas H ^b   Reinke, Donald ^b  

^a Engineering Research Center and Hydraulics Laboratory   (United States)

^b Department of Environmental and Radiological Health Sciences   (United States)

Author keywords

Cloud classification; Maximum likelihood; Probabilistic neural networks (PNNs); Singular value decomposition (SVD); Temporal updating

Indexed keywords

CLOUDS; FEATURE EXTRACTION; GEOSTATIONARY SATELLITES; INFRARED IMAGING; MARKOV PROCESSES; MAXIMUM LIKELIHOOD ESTIMATION; PROBABILITY;

CLOUD CLASSIFICATION; PROBABILISTIC NEURAL NETWORKS; SINGULAR VALUE DECOMPOSITION; TEMPORAL UPDATING;

NEURAL NETWORKS;

EID: 0035439748     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.950147     Document Type: Article

References (15)

1. Temporal updating scheme for probabilistic neural network with application to satellite cloud classification
2. A study of neural network-based cloud classification using textural and spectral features
3. Pattern recognition techniques for identification of cloud and cloud systems
4. A multi-spectral cloud type identification method developed for nimbus 3 mrir measurements
5. Cloud field classification based upon high spatial resolution textural feature (I): Gray-level coocurrence matrix approach
6. A comparative study of cloud classification techniques
7. Performance evaluation of texture measures for ground cover identification in satellite images by means of a neural network classifier
8. Improved cloud detection in GOES scenes over land
9. Improved cloud detection in GOES scenes over the oceans
10. A neural network approach to cloud classification
11. Cloud classification of AVHRR imagery in maritime regions using a probabilistic neural network
12. Classification with spatio-temporal interpixel class dependency contexts
13. Bayesian classification in a time-varying environment
14. Utilizing multi-temporal data by a stochastic model
15. Generalization accuracy of probabilistic neural networks compared with backpropagation networks