Deep learning-based explainable target classification for synthetic aperture radar images

Mandeep*, Husanbir Singh Pannu, Avleen Malhi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)
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Abstract

Deep learning has been extensively useful for its ability to mimic the human brain to make decisions. It is able to extract features automatically and train the model for classification and regression problems involved with complex images databases. This paper presents the image classification using Convolutional Neural Network (CNN) for target recognition using Synthetic-aperture Radar (SAR) database along with Explainable Artificial Intelligence (XAI) to justify the obtained results. In this work, we experimented with various CNN architectures on the MSTAR dataset, which is a special type of SAR images. Accuracy of target classification is almost 98.78% for the underlying preprocessed MSTAR database with given parameter options in CNN. XAI has been incorporated to explain the justification of test images by marking the decision boundary to reason the region of interest. Thus XAI based image classification is a robust prototype for automatic and transparent learning system while reducing the semantic gap between soft-computing and humans way of perception.

Original languageEnglish
Title of host publicationProceedings - 2020 13th International Conference on Human System Interaction, HSI 2020
PublisherIEEE Computer Society
Pages34-39
Number of pages6
ISBN (Electronic)9781728173924
DOIs
Publication statusPublished - Jun 2020
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Human System Interaction - Tokyo, Japan
Duration: 6 Jun 20208 Jun 2020
Conference number: 13

Publication series

NameConference on Human System Interaction
PublisherIEEE
ISSN (Print)2158-2246
ISSN (Electronic)2158-2254

Conference

ConferenceInternational Conference on Human System Interaction
Abbreviated titleHSI
CountryJapan
CityTokyo
Period06/06/202008/06/2020

Keywords

  • Artificial intelligence
  • deep learning
  • image classification
  • target recognition
  • synthetic aperture radar
  • SAR TARGET

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