Finding Ship Radars in SAR Images: Localizing Radio Frequency Interference Using Unsupervised Deep Learning

Sørensen, Kristian Aa. and Kusk, Anders and Heiselberg, Henning and Heiselberg, Peder (2022) Finding Ship Radars in SAR Images: Localizing Radio Frequency Interference Using Unsupervised Deep Learning. In: 31st Annual Newfoundland Electrical and Computer Engineering Conference, November 15, 2022, St. John's, Newfoundland and Labrador.

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Synthetic Aperture Radar (SAR) satellite images are used increasingly more to observe the maritime environment, but they sometimes experience image degradation caused by interfering signals from external radars. Few on-ground radars can cause Radio Frequency Interference (RFI) and the RFI information can therefore increase domain awareness. Localizing and characterizing RFI signals in the ocean might help classify otherwise overlooked ships as, e.g., potential navy ships. In this study, we detect and localize RFI signals automatically in Sentinel-1 quick-look images. The spatial structure of RFI signals vary greatly and unsupervised deep learning was therefore used to reconstruct RFI-free Sentinel-1 images. Anomaly heat-maps were then computed to localize RFI anomalies in the images under varying environmental and geographical conditions. We localized several RFI signals mid-sea believed to be caused by ship-borne air-surveillance radars. This study shows that more information can be extracted from certain detected objects, such as ships, from SAR images.

Item Type: Conference or Workshop Item (Paper)
Item ID: 15929
Additional Information: All authors have consented to publication in the Research Repository
Keywords: synthetic aperture radar (SAR), radio frequency interference (RFI), deep learning, convolutional autoencoder, anomaly classification and localization
Department(s): Engineering and Applied Science, Faculty of
Date: 2022
Date Type: Submission

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