Efficient ATR Using Contrastive Learning

Attiano Purpura-Pontoniere, Abdullah Al Zubaer Imran, Tarun Bhattacharya

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

1 Scopus citations


Automatic Target Recognition (ATR) is a valuable application of computer vision that traditionally requires copious and tedious labeling through supervised learning. This research explored if ATR can be performed on satellite imagery at a comparable accuracy to a fully supervised baseline model with a considerably smaller subset of data labelled, on the order of 10%, using a recently developed semi-supervised technique, contrastive learning. Supervised contrastive loss was explored and compared to traditional cross entropy loss. Supervised contrastive loss was found to perform significantly better with a subset of the data labelled on the XView dataset, a publicly available dataset of satellite imagery captured with .3 meter ground sampling. The caveats when nothing and everything is labelled were additionally explored. [1].

Original languageEnglish
Title of host publicationAutomatic Target Recognition XXXII
EditorsRiad I. Hammoud, Timothy L. Overman, Abhijit Mahalanobis, Kristen Jaskie
ISBN (Electronic)9781510650688
StatePublished - 2022
EventAutomatic Target Recognition XXXII 2022 - Virtual, Online
Duration: Jun 6 2022Jun 12 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceAutomatic Target Recognition XXXII 2022
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2022 SPIE. All rights reserved.


  • ATR
  • Classification
  • Computer Vision
  • Contrastive Learning
  • Satellite Imagery
  • Self-Supervised Learning
  • Semi-Supervised Learning

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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