$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

선별적인 임계값 선택을 이용한 준지도 학습의 SAR 분류 기술
Semi-Supervised SAR Image Classification via Adaptive Threshold Selection 원문보기

韓國軍事科學技術學會誌 = Journal of the KIMST, v.27 no.3, 2024년, pp.319 - 328  

도재준 (한국항공대학교 인공지능학과) ,  유민정 (한국항공대학교 인공지능학과) ,  이재석 (한화시스템(주) 레이다연구소) ,  문효이 (한화시스템(주) 레이다연구소) ,  김선옥 (한국항공대학교 인공지능학과)

Abstract AI-Helper 아이콘AI-Helper

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....

주제어

참고문헌 (28)

  1. F, Pierre, et al., "Sharpness-aware minimization for efficiently improving generalization," arXiv preprint arXiv:2010.01412, 2020.? 

  2. M, Wortsman, et al., "Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time," International Conference on Machine Learning, PMLR, 2022.? 

  3. H, M. Kabir, "Reduction of Class Activation Uncertainty with Background Information," arXiv preprint arXiv:2305.03238, 2023.? 

  4. Z, Yang, et al., "SAR image classification method based on improved capsule network," Journal of Physics: Conference Series, Vol. 1693, No. 1, IOP Publishing, 2020.? 

  5. S, Chen, et al., "Target classification using the deep convolutional networks for SAR images," IEEE transactions on geoscience and remote sensing 54.8, 4806-4817, 2016.? 

  6. H, Ren, et al., "Extended convolutional capsule network with application on SAR automatic target recognition," Signal Processing 183 : 108021, 2021.? 

  7. Q, Xie, et al., "Self-training with noisy student improves imagenet classification," Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020.? 

  8. E, Arazo, et al., "Pseudo-labeling and confirmation bias in deep semi-supervised learning," 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 2020.? 

  9. D, Berthelot, et al., "Mixmatch: A holistic approach to semi-supervised learning," Advances in neural information processing systems 32, 2019.? 

  10. D, Berthelot, et al., "Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring," arXiv preprint arXiv:1911.09785, 2019.? 

  11. K, Sohn, Kihyuk, et al., "Fixmatch: Simplifying semi-supervised learning with consistency and confidence," Advances in neural information processing systems 33 : 596-608, 2020.? 

  12. T, Chen, et al., "A simple framework for contrastive learning of visual representations," International conference on machine learning, PMLR, 2020.? 

  13. R, Shams, "Semi-supervised classification for natural language processing," arXiv preprint arXiv:1409.7612, 2014.? 

  14. A, Anaby-Tavor, et al., "Do not have enough data? Deep learning to the rescue!," Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, No. 05, 2020.? 

  15. B, Zhang, et al., "Censer: Curriculum semi-supervised learning for speech recognition based on self-supervised pre-training," arXiv preprint arXiv:2206.08189, 2022.? 

  16. V, Tsouvalas, et al., "Federated self-training for semi-supervised audio recognition," ACM Transactions on Embedded Computing Systems 21.6 : 1-26, 2022.? 

  17. M, Sajjadi, et al., "Regularization with stochastic transformations and perturbations for deep semi-supervised learning," Advances in neural information processing systems 29, 2016.? 

  18. X, Wang, et al., "Contrastive learning with stronger augmentations," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.? 

  19. E, Cubuk, et al., "Randaugment: Practical automated data augmentation with a reduced search space," Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, 2020.? 

  20. ED, Cubuk, et al., "Autoaugment: Learning augmentation strategies from data," Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019.? 

  21. Y, Lei, et al., "Synthetic Images Augmentation for Robust SAR Target Recognition," 2021 The 5th International Conference on Video and Image Processing, 2021.? 

  22. C, Hyunho, and J, Jechang, et al., "Speckle noise reduction technique for SAR images using statistical characteristics of speckle noise and discrete wavelet transform," Remote Sensing 11.10 (2019): 1184.? 

  23. X, Zhang, et al., "A Novel Data Augmentation Method for SAR Image Target Detection and Recognition," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, IEEE, 2021.? 

  24. M, Zhang, et al., "Data augmentation method of SAR image dataset," IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2018.? 

  25. K, Choi, et al., "Deep Cascade Network for Noise-Robust SAR Ship Detection With Label Augmentation," in IEEE Geoscience and Remote Sensing Letters, Vol. 19, pp. 1-5, 2022, Art No. 4514005, doi: 10.1109/LGRS.2022.3205715.? 

  26. ER, Keydel, et al., "MSTAR extended operating conditions: A tutorial," Algorithms for Synthetic Aperture Radar Imagery III 2757, 228-242, 1996.? 

  27. S, Zagoruykond Nikos Komodakis, "Wide residual networks," arXiv preprint arXiv:1605.07146, 2016.? 

  28. C, Coman, "A deep learning SAR target classification experiment on MSTAR dataset," 2018 19th international radar symposium(IRS), IEEE, 2018. 

관련 콘텐츠

오픈액세스(OA) 유형

GOLD

오픈액세스 학술지에 출판된 논문

이 논문과 함께 이용한 콘텐츠

저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로