Automatic Target Recognition from SAR Images Using Capsule Networks

  • Rutvik Shah
  • Akshit Soni
  • Vinod Mall
  • Tushar GadhiyaEmail author
  • Anil K. Roy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11942)


Synthetic Aperture Radar (SAR) imagery has become popular in the past few decades owing to its operability under difficult weather conditions. In this paper, we introduce a deep learning architecture using a Capsule Network (CapsNet) for automatic target recognition (ATR) of images of targets captured using an X-band SAR sensor. The architecture consists of a single convolutional layer, followed by two Capsule layers, and a decoder network at the end. Since, traditional Convolutional Neural Networks (CNNs) often require a significant number of training images, their performance is limited for small number of training examples. Unlike CNNs, Capsule Networks encapsulate the instantiation parameters of an object within an image, thus, they do not require a large number of training samples. In addition, Capsule Networks are view-point invariant. For the evaluation of the proposed method, we have used the MSTAR database, containing SAR images of 10-classes of military vehicles. We have achieved 98.14% overall classification accuracy on this dataset.


Capsule Network Synthetic Aperture Radar Automatic target recognition Convolutional Neural Network (CNN) 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rutvik Shah
    • 1
  • Akshit Soni
    • 1
  • Vinod Mall
    • 2
  • Tushar Gadhiya
    • 1
    Email author
  • Anil K. Roy
    • 1
  1. 1.Dhirubhai Ambani Institute of Information and Communication TechnologyGandhinagarIndia
  2. 2.ADG of Police, Government of GujaratGandhinagarIndia

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