Shuffle GAN With Autoencoder: A Deep Learning Approach to Separate Moving and Stationary Targets in SAR Imagery

Synthetic aperture radar (SAR) has been widely applied in both civilian and military fields because it provides high-resolution images of the ground target regardless of weather conditions, day or night. In SAR imaging, the separation of moving and stationary targets is of great significance as it i...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2022-09, Vol.33 (9), p.4770-4784
1. Verfasser:	Pu, Wei
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Clutter Deep learning Gallium nitride Generative adversarial networks Image resolution Imaging Machine learning Military communications Moving and stationary targets separation Moving targets Radar Radar imaging Radar polarimetry Separation shuffle generative adversarial network (GAN) Signal processing Synthetic aperture radar synthetic aperture radar (SAR) Task analysis Weather
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