Moving Target Shadow Detection using Transformer in Video Sar

Video synthetic aperture radar (SAR) has been found to be very valuable for detecting and tracking moving targets and observing areas of interest. Shadows produced by target motion in sequential radar images can be used to detect targets themselves. Since existing deep learning shadow detection meth...

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Vydané v:IEEE International Geoscience and Remote Sensing Symposium proceedings s. 2614 - 2617
Hlavní autori: Wang, Wei, Zhou, Yuanyuan, Xie, Zhikun, Zhang, Tianwen, Shi, Jun, Zhang, Xiaoling
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 17.07.2022
Predmet:
Deep learning
Feature extraction
Laboratories
moving target
Radar detection
Radar imaging
shadow detection
Target tracking
transformer
Transformers
Video SAR
ISSN:2153-7003
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Popis
Shrnutí:Video synthetic aperture radar (SAR) has been found to be very valuable for detecting and tracking moving targets and observing areas of interest. Shadows produced by target motion in sequential radar images can be used to detect targets themselves. Since existing deep learning shadow detection methods often require many hand-designed components, in this paper, we propose a shadow detection method for video SAR moving target based on transformer, which is named Deformable Shadow-DETR. Deformable Shadow-DETR can better extract shadow features, and use the transformer encoder-decoder network to treat shadow detection as a direct set prediction problem, eliminating the need for cumbersome hand-designed components. Experiments on the real video SAR data published by the Sandia National Laboratories show that our proposed moving target shadow detection method can achieve excellent performance.
ISSN:2153-7003
DOI:10.1109/IGARSS46834.2022.9884510