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      Deep Learning as Applied in SAR Target Recognition and Terrain Classification

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          Abstract

          Deep learning such as deep neural networks has revolutionized the computer vision area. Deep learning-based algorithms have surpassed conventional algorithms in terms of performance by a significant margin. This paper reviews our works in the application of deep convolutional neural networks to target recognition and terrain classification using the SAR image. A convolutional neural network is employed to automatically extract a hierarchic feature representation from the data, based on which the target recognition and terrain classification can be conducted. Experimental results on the MSTAR benchmark dataset reveal that deep convolutional network could achieve a state-of-the-art classification accuracy of 99% for the 10-class task. For a polarimetric SAR image classification, we propose complex-valued convolutional neural networks for complex SAR images. This algorithm achieved a state-of-the-art accuracy of 95% for the 15-class task on the Flevoland benchmark dataset.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 April 2017
          : 6
          : 2
          : 136-148
          Affiliations
          [1 ] (Key Laboratory for Information Science of Electromagnetic Waves, Fudan University, Shanghai 200433, China)
          Article
          47b66704e4a94567a4f9e0f735dc8740
          10.12000/JR16130

          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

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