Blog
About

5
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      SAR Target Recognition with Feature Fusion Based on Stacked Autoencoder

      Read this article at

      ScienceOpenPublisherDOAJ
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 April 2017
          : 6
          : 2
          : 167-176
          Affiliations
          [1 ] ①(School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China)
          [2 ] ②(Beijing Institute of Remote Sensing Information, Beijing 100192, China)
          Article
          c3e47e9fb46a4e05a4a4638a5a0ef2a0
          10.12000/JR16112

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

          Comments

          Comment on this article