摘要
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 compo- nents of exo-atmospheric targets is demonstrated by applying the probabilistic neural network. Differences in thermM 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.
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 compo- nents of exo-atmospheric targets is demonstrated by applying the probabilistic neural network. Differences in thermM 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.
基金
supported by the National Natural Science Foundation of China (No. 60877065)
the Research Fund for the Doctoral Program of Higher Education of China (No. 20092302110026)
the Key Laboratory of All Optical Network and Advanced Telecommunication Network, Ministry of Education of China
