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Proceedings Paper

Bayesian multi-nets classifier in the interpretation of remote sensing images
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Paper Abstract

In the traditional BNC model, the relationship between the attributes are the same for all the instances of the class variable C. BMN classifier is a generalized form of BNC, in the sense that it allows different relationships among attributes for every values of the class variable, and provides a unique net structure for every object class. This paper proposes Bayesian Multi-nets (BMN) Models based on the analysis of conditional mutual information(CMI) between image features of different objects classes, and constructs BMN classifier for remote sensing images on the basis of experiment. Classification accuracy of single objects in BMN classifier outperforms that of traditional BN, proves the latent value of the proposed models in the classification of remote sensing images.

Paper Details

Date Published: 29 December 2008
PDF: 10 pages
Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72850G (29 December 2008); doi: 10.1117/12.815883
Show Author Affiliations
Jianbin Tao, Wuhan Univ. (China)
Ning Shu, Wuhan Univ. (China)
Zhaoqing Shen, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 7285:
International Conference on Earth Observation Data Processing and Analysis (ICEODPA)
Deren Li; Jianya Gong; Huayi Wu, Editor(s)

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