Abstract
Gaussian process was developed from bayesian neural networks with the infinite number of nodes in the hidden layer. It is also an bayesian model averaging approach which integrate the model prediction with the posterior probability of the parameters. In this paper, the basic theory of gaussian process for classifying satellite remote sensing data is introduced and experimented using the multi-temporal LANDSAT TM, JERS1 and ERS1 SAR data. The accuracies of the classifications have been compared with the maximum likelihood method and bayesian neural network method. The result shows that the gaussian process outperforms the other methods for classifying the LANDSAT/TM, JERS-1/SAR, and ERS-1/AMldata, and especially performs well for the sensor fusion data.
