The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4, 325–328, 2014
https://doi.org/10.5194/isprsarchives-XL-4-325-2014
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4, 325–328, 2014
https://doi.org/10.5194/isprsarchives-XL-4-325-2014

  23 Apr 2014

23 Apr 2014

Remote Sensing Image Classification of Geoeye-1 High-Resolution Satellite

B. Yang1,2 and X. Yu1,2 B. Yang and X. Yu
  • 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.