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Proceedings Paper

Detection of buried objects in multi-temporal and multi-band infrared imagery using dynamic Bayesian networks
Author(s): Shibo Gao; Yongqiang Zhao; Kun Wei; Yongmei Cheng
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Paper Abstract

A direct change detection method that utilizes the dynamic Bayesian network (DBNs) is proposed to detect buried objects. The DBNs uses the time series dynamic data to produce credible probabilistic reasoning, and is developed to utilize the IR images obtained by different band and temporal. The proposed method offers a way to change detection analysis from the static viewpoint to the dynamic viewpoint, which can input and deal with more than two multi-temporal images simultaneously which are featured by multi-band. The origin of thermal contrast in infrared imaging between the buried objects and background is illuminated on the theory of infrared radiation. The differences of temperature can be captured by multi-temporal and multi-band infrared images. The IR images of the regions of interest (ROI) acquired at three different times as inputs to detect buried objects using multi-temporal direct change detection based on physical principle of infrared imaging. The experimental results indicate that the change detection method based on DBNs is an effective to buried objects detection.

Paper Details

Date Published: 15 November 2007
PDF: 9 pages
Proc. SPIE 6787, MIPPR 2007: Multispectral Image Processing, 67871H (15 November 2007); doi: 10.1117/12.749868
Show Author Affiliations
Shibo Gao, Northwestern Polytechnical Univ. (China)
Yongqiang Zhao, Northwestern Polytechnical Univ. (China)
Kun Wei, Northwestern Polytechnical Univ. (China)
Yongmei Cheng, Northwestern Polytechnical Univ. (China)

Published in SPIE Proceedings Vol. 6787:
MIPPR 2007: Multispectral Image Processing
Henri Maître; Hong Sun; Jianguo Liu; Enmin Song, Editor(s)

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