• Chinese Optics Letters
  • Vol. 9, Issue 7, 070101 (2011)
Jianlai Wang and Chunling Yang
Author Affiliations
  • School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
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    DOI: 10.3788/COL201109.070101 Cite this Article Set citation alerts
    Jianlai Wang, Chunling Yang. Exo-atmospheric target discrimination using probabilistic neural network[J]. Chinese Optics Letters, 2011, 9(7): 070101 Copy Citation Text show less

    Abstract

    Exo-atmospheric targets are especially difficult to distinguish using currently available techniques, because all target parts follow the same spatial trajectory. The feasibility of distinguishing multiple type components of exo-atmospheric targets is demonstrated by applying the probabilistic neural network. Differences in thermal behavior and time-varying signals of space-objects are analyzed during the selection of features used as inputs of the neural network. A novel multi-colorimetric technology is introduced to measure precisely the temporal evolutional characteristics of temperature and emissivity-area products. To test the effectiveness of the recognition algorithm, the results obtained from a set of synthetic multispectral data set are presented and discussed. These results indicate that the discrimination algorithm can obtain a remarkable success rate.
    Jianlai Wang, Chunling Yang. Exo-atmospheric target discrimination using probabilistic neural network[J]. Chinese Optics Letters, 2011, 9(7): 070101
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