Volume 6 Issue 2
May  2017
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Article Navigation >  Journal of Radars  > 2017  >  6(2): 167-176
Kang Miao, Ji Kefeng, Leng Xiangguang, Xing Xiangwei, Zou Huanxin. SAR Target Recognition with Feature Fusion Based on Stacked Autoencoder[J]. Journal of Radars, 2017, 6(2): 167-176. doi: 10.12000/JR16112
Citation: Kang Miao, Ji Kefeng, Leng Xiangguang, Xing Xiangwei, Zou Huanxin. SAR Target Recognition with Feature Fusion Based on Stacked Autoencoder[J]. Journal of Radars, 2017, 6(2): 167-176. doi: 10.12000/JR16112
shu

SAR Target Recognition with Feature Fusion Based on Stacked Autoencoder

doi: 10.12000/JR16112
  • ①.

    School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China

  • ②.

    Beijing Institute of Remote Sensing Information, Beijing 100192, China

Funds:  The National Natural Science Foundation of China (61372163, 61331015, 61601035)
  • Received Date: 2016-09-29
  • Rev Recd Date: 2017-01-24
    • Available Online: 2017-03-22
  • Publish Date: 2017-04-28
    Abstract
  • A feature fusion algorithm based on a Stacked AutoEncoder (SAE) for Synthetic Aperture Rader (SAR) imagery is proposed in this paper. Firstly, 25 baseline features and Three-Patch Local Binary Patterns (TPLBP) features are extracted. Then, the features are combined in series and fed into the SAE network, which is trained by a greedy layer-wise method. Finally, the softmax classifier is employed to fine tune the SAE network for better fusion performance. Additionally, the Gabor texture features of SAR images are extracted, and the fusion experiments between different features are carried out. The results show that the baseline features and TPLBP features have low redundancy and high complementarity, which makes the fused feature more discriminative. Compared with the SAR target recognition algorithm based on SAE or CNN (Convolutional Neural Network), the proposed method simplifies the network structure and increases the recognition accuracy and efficiency. 10-classes SAR targets based on an MSTAR dataset got a classification accuracy up to 95.88%, which verifies the effectiveness of the presented algorithm.

     

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