Deep-learning-based synthetic aperture radar automatic target recognition (SAR-ATR) plays
a significant role in the military and civilian fields. However, data limitation and large …

Sufficient synthetic aperture radar (SAR) target images are very important for the
development of research works. However, available SAR target images are often limited in …

Convolutional neural networks (CNNs) have achieved high performance in synthetic
aperture radar (SAR) automatic target recognition (ATR). However, the performance of …

Data-driven automatic target recognition (ATR) methods have become the mainstream in the
synthetic aperture radar (SAR) community at this stage. However, in real SAR application …

Deep learning is a compelling technique for feature extraction due to its adaptive capacity of
processing and providing deeper image information. However, for the task of leaf cultivar …

In recent years, significant research has been conducted on utilizing simulated data to
support synthetic aperture radar automatic target recognition (SAR-ATR) based on deep …

Synthetic aperture radar (SAR) images often lack sufficient attention to target features,
inadequately express target feature information, and neglect phase information in …

Deep learning has emerged as the dominant paradigm for synthetic aperture radar (SAR)
automatic target recognition (ATR). However, existing learning algorithms focus primarily on …

Considering the constraints inherent in transfer learning for few-shot Synthetic Aperture
Radar (SAR) target recognition, we have investigated the possibilities presented by …

Sufficient training samples are prerequisite for most existing automatic target recognition
(ATR) algorithms to obtain satisfactory recognition performance. Nevertheless, sometimes …