Journal of the Korea Institute of Military Science and Technology (한국군사과학기술학회지)

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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Semi-Supervised SAR Image Classification via Adaptive Threshold Selection

선별적인 임계값 선택을 이용한 준지도 학습의 SAR 분류 기술

  • Jaejun Do (Department of Artificial Intelligence, Korea Aerospace University) ;
  • Minjung Yoo (Department of Artificial Intelligence, Korea Aerospace University) ;
  • Jaeseok Lee (Radar Research and Development Center, Hanwha Systems Co., Ltd.) ;
  • Hyoi Moon (Radar Research and Development Center, Hanwha Systems Co., Ltd.) ;
  • Sunok Kim (Department of Artificial Intelligence, Korea Aerospace University)
  • 도재준 (한국항공대학교 인공지능학과) ;
  • 유민정 (한국항공대학교 인공지능학과) ;
  • 이재석 (한화시스템(주) 레이다연구소) ;
  • 문효이 (한화시스템(주) 레이다연구소) ;
  • 김선옥 (한국항공대학교 인공지능학과)
  • Received : 2023.08.09
  • Accepted : 2024.03.15
  • Published : 2024.06.05
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Abstract

Semi-supervised learning is a good way to train a classification model using a small number of labeled and large number of unlabeled data. We applied semi-supervised learning to a synthetic aperture radar(SAR) image classification model with a limited number of datasets that are difficult to create. To address the previous difficulties, semi-supervised learning uses a model trained with a small amount of labeled data to generate and learn pseudo labels. Besides, a lot of number of papers use a single fixed threshold to create pseudo labels. In this paper, we present a semi-supervised synthetic aperture radar(SAR) image classification method that applies different thresholds for each class instead of all classes sharing a fixed threshold to improve SAR classification performance with a small number of labeled datasets.

Keywords

Acknowledgement

이 연구는 2022년 정부(방위사업청)의 재원으로 국방과학연구소의 지원을 받아 수행된 미래도전국방기술연구개발사업임(No. 915029201).

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