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Self-Supervised Learning Based Anomaly Detection in Synthetic Aperture Radar Imaging

  • Published:2022 Issue: Volume:3 Page:440-449
  • ISSN:2644-1322
  • Container-title:IEEE Open Journal of Signal Processing
  • language:
  • Short-container-title:IEEE Open J. Signal Process.

Author:

Muzeau Max1ORCID,Ren Chengfang2ORCID,Angelliaume Sebastien2ORCID,Datcu Mihai3ORCID,Ovarlez Jean-Philippe1ORCID

Affiliation:

1. SONDRA, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France

2. DEMR, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France

3. University Politehnica of Bucharest (UPB), Romania and German Aerospace Center (DLR), Wessling, Germany

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Signal Processing

Reference39 articles.

1. Cyclical Learning Rates for Training Neural Networks

2. Adam: A method for stochastic optimization;kingma;Proc Int Conf Learn Representations,0

3. An Overview of Covariance‐based Change Detection Methodologies in Multivariate SAR Image Time Series

4. Generative adversarial networks

5. Adversarial autoencoders;makhzani;Proc Int Conf Learn Representations,0

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1. General Feature Extraction In SAR Target Classification: A Contrastive Learning Approach Across Sensor Types;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

2. A Comprehensive Review on Anomaly Detection in Images: Challenges and Future Research Directions;2023 IEEE North Karnataka Subsection Flagship International Conference (NKCon);2023-11-19

3. Self-Supervised SAR Anomaly Detection Guided with RX Detector;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16

4. Impact of PolSAR Pre-Processing and Balancing Methods on Complex-Valued Neural Networks Segmentation Tasks;IEEE Open Journal of Signal Processing;2023
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