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

Jianbin Tao; Ning Shu; Zhaoqing Shen

doi:10.1117/12.815883

29 December 2008 Bayesian multi-nets classifier in the interpretation of remote sensing images

Jianbin Tao, Ning Shu, Zhaoqing Shen

Proceedings Volume 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA); 72850G (2008) https://doi.org/10.1117/12.815883
Event: International Conference on Earth Observation Data Processing and Analysis, 2008, Wuhan, China

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.

Citation Download Citation

Jianbin Tao, Ning Shu, and Zhaoqing Shen "Bayesian multi-nets classifier in the interpretation of remote sensing images", Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72850G (29 December 2008); https://doi.org/10.1117/12.815883

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KEYWORDS

Remote sensing

Image classification

Bismuth

Information theory

Absorption

Earth observing sensors

Landsat

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