Remote Sensing Image Classification of Geoeye-1 High-Resolution Satellite
- 1Beijing Institute of Surveying and Mapping, Beijing, 100038, China
- 2Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing, 100038, China
Keywords: Texture Classification, Pattern Recognition, High-resolution, Satellite Image
Abstract. Networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Bayesian Networks Augmented Naive Bayes (BAN) to texture classification of High-resolution satellite images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. In the experiment, we choose GeoEye-1 satellite images. Experimental results demonstrate BAN outperform than NBC in the overall classification accuracy. Although it is time consuming, it will be an attractive and effective method in the future.