Chinese Optics Letters, 2011, 9 (7): 070101, Published Online: May. 27, 2011
Exo-atmospheric target discrimination using probabilistic neural network Download: 653次
大气层外目标识别 多光谱数据融合 概率神经网络 010.0280 Remote sensing and sensors 100.4996 Pattern recognition, neural networks 300.6340 Spectroscopy, infrared
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.