Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4, 325-328, 2014
https://doi.org/10.5194/isprsarchives-XL-4-325-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
23 Apr 2014
Remote Sensing Image Classification of Geoeye-1 High-Resolution Satellite
B. Yang1,2 and X. Yu1,2 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.
Conference paper (PDF, 692 KB)


Citation: Yang, B. and Yu, X.: Remote Sensing Image Classification of Geoeye-1 High-Resolution Satellite, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4, 325-328, https://doi.org/10.5194/isprsarchives-XL-4-325-2014, 2014.

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