Exo-atmospheric Infrared Objects Classification Based on Dual-channel LSTM Network

Abstract In order to realize accurate classification of Exo-atmospheric objects, a new framework which depends on dual-channel LSTM(Long Short-Term Memory) is presented in this paper. The influencing factors of Exo-atmospheric objects infrared imaging are analyzed and the simulation model of infrared image sequence is studied, which establishes the data foundation for the following research. After analyzing the characteristics of exo-atmospheric objects’ gray series, the input sequences are down-sampled and truncated separately at first, and then fed into corresponding LSTM channels to extract the global and local features respectively. Finally, the features of two channels are fused to classify the targets. Experimental results show that the proposed framework achieves better classification performance, and due to its good computing structure, it also has good practicability.