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      Shared Representation of SAR Target and Shadow Based on Multilayer Auto-encoder

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          Abstract

          Automatic Target Recognition (ATR) of Synthetic Aperture Radar (SAR) image is investigated. A SAR feature extraction algorithm based on multilayer auto-encoder is proposed. The method makes use of a probabilistic neural network, Restricted Boltzmann Machine (RBM) modeling probability distribution of environment. Through the formation of more expressive multilayer neural network, the deep learning model learns shared representation of the target and its shadow outline reflecting the target shape characteristics. Targets are classified automatically through two recognition models. The experiment results based on the MSTAR verify the effectiveness of proposed algorithm.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 June 2013
          : 2
          : 2
          : 195-202
          Affiliations
          [1 ] Electronic Engineering Institute
          Article
          d96d44685d7a45e5a59c0a58cf3b9f17
          10.3724/SP.J.1300.2012.20085

          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

          Remote sensing, Electrical engineering

          SAR, Shadow, Multilayer auto-encoder, Feature extraction

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