CUI Jin-hui, SUN hui. Background distribution of multi-spectral detector output[J]. Journal of Applied Optics, 2010, 31(5): 748-751.
Citation: CUI Jin-hui, SUN hui. Background distribution of multi-spectral detector output[J]. Journal of Applied Optics, 2010, 31(5): 748-751.
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Background distribution of multi-spectral detector output

  • 1.

    Research Center for Space Optical Engineering,Harbin Institute of Technology,Harbin 150001,China;

  • 2.

    College of Science,Harbin Engineering University,Harbin 150001,China;

  • 3.

    Teaching Center of Electrics and Electronics,Harbin University of Science and Technology,Harbin 150001,China

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    • Abstract
  • In the detection of field fire or leakage, the target signature is very weak. Analyzing statistical models of actual spectral detector output distribution is crucial for setting detection thresholds, selecting statistical classifiers and designing constant false alarm rate detectors. Multispectral image data is processed with the CEM algorithm. Statistical model of detector output is discussed based on the stochastic mixing model. It is concluded that Gaussian mixture model is suitable. This is demonstrated with the ETM Satellite date. EM algorithm is adopted to estimate parameters of Gaussian mixture model. Computer simulation results show that the probability density function of statistics derived from Gaussian mixture model agrees with that of actual data. Curves of false alarm rate also show that Gaussian mixture model is effective at the condition of Pfa＞10-4.
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    • References (1)
  • ［1］YING LI, ANTHONY VODACEK, ROBERT L. KREMENS, et al. A hybrid contextual approach to wildland fire detection using multispectral imagery［J］. IEEE Transactions on Geoscience and Remote Sensing, 2005,43(9):2115-2126.
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    ［5］MANOLAKIS D, ROSSACCI M, CIPAR J, et al. Statistical characterization of natural hyperspectral backgrounds using t-elliptically contoured distributions［J］. SPIE, 2005,5806:56-65.
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