A neural-statistical approach to multitemporal and multisource remote-sensing image classification
- 1 May 1999
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 37 (3), 1350-1359
- https://doi.org/10.1109/36.763299
Abstract
No abstract availableThis publication has 22 references indexed in Scilit:
- An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing imagesIEEE Transactions on Geoscience and Remote Sensing, 1997
- The expectation-maximization algorithmIEEE Signal Processing Magazine, 1996
- Classification of multisensor remote-sensing images by structured neural networksIEEE Transactions on Geoscience and Remote Sensing, 1995
- The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenonIEEE Transactions on Geoscience and Remote Sensing, 1994
- Classification with spatio-temporal interpixel class dependency contextsIEEE Transactions on Geoscience and Remote Sensing, 1992
- Neural Network Classifiers Estimate Bayesian a posteriori ProbabilitiesNeural Computation, 1991
- Spatial-temporal Autocorrelated Model For Contextual ClassificationIEEE Transactions on Geoscience and Remote Sensing, 1990
- Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing DataIEEE Transactions on Geoscience and Remote Sensing, 1990
- A means for utilizing ancillary information in multispectral classificationRemote Sensing of Environment, 1982
- The use of prior probabilities in maximum likelihood classification of remotely sensed dataRemote Sensing of Environment, 1980