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

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

In this paper, we proposed to investigate unsupervised anomaly detection in Synthetic Aperture Radar (SAR) images. Our approach considers anomalies as abnormal patterns that deviate from their surroundings without prior knowledge of their characteristics. This method deals with the crucial problems...

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Bibliographic Details
Main Authors: Max Muzeau, Chengfang Ren, Sebastien Angelliaume, Mihai Datcu, Jean-Philippe Ovarlez
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Open Journal of Signal Processing
Subjects:
Adversarial autoencoder
anomaly detection
deep-learning
despeckling
SAR
self-supervised
Online Access:https://ieeexplore.ieee.org/document/9987646/

Internet

https://ieeexplore.ieee.org/document/9987646/

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