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Vol. 168, Issue 4, April 2014, pp. 23-29




Remote Sensing Image Classification by Bayesian Network Classifier Based on Causality

1 Wang Qiuquan, 2 Guo Li, 3 Li Xiang

1 West Section Headquarters, China Transportation Construction Co., Ltd., Xian, 710065, China
2 School of Economics and Management, China University of Geosciences, Wuhan, 430074, China
3 School of Computer, China University of Geosciences, Wuhan, 430074, China
1 Tel.:15991169521

E-mail: lixiang@cug.edu.cn


Received: 25 December 2013 /Accepted: 28 February 2014 /Published: 30 April 2014

Digital Sensors and Sensor Sysstems


Abstract: It has always been a hotspot and difficult point in remote sensing to identify interesting geographical objects from remote sensing images. To reduce the independence between the random variables in the network Bayesian classifier model and to improve the classification performance, a causality-based network Bayesian classifier is suggested in this paper. In this model, the improved genetic algorithm is used for network topology learning and causality analyzing is taken for feature selection, which aims to realizing automatic recognition of unfavorable geological bodies. Experiments show that this model is of good classification performance and of high classification stability.


Keywords: Remote sensing, Bayesian network, Genetic algorithm, Adverse geological.


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