文章基本信息

标题：Remote Sensing Image Classification of Geoeye-1 High-Resolution Satellite
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作者：B. Yang ; X. Yu
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2014
卷号：XL-4
页码：325-328
DOI：10.5194/isprsarchives-XL-4-325-2014
出版社：Copernicus Publications
摘要：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.
关键词：Texture Classification; Pattern Recognition; High-resolution; Satellite Image