Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate
construction materials and provide detailed geographic information. In remote sensing …

In recent years, convolutional neural networks (CNNs) have drawn considerable attention
for the analysis of synthetic aperture radar (SAR) data. In this study, major subareas of SAR …

Synthetic aperture radar (SAR) target recognition faces the challenge that there are very little
labeled data. Although few-shot learning methods are developed to extract more information …

Deep learning has been widely used in automatic target recognition (ATR) for synthetic
aperture radar (SAR) recently. However, most of the studies are based on the network …

Deep neural network (DNN)-based synthetic aperture radar (SAR) automatic target
recognition (ATR) systems have been shown to be highly vulnerable to adversarial …

Machine learning methods for synthetic aperture radar (SAR) image automatic target
recognition (ATR) can be divided into two main types: traditional methods and deep learning …

In synthetic aperture radar automatic target recognition (SAR-ATR), target information is
usually propagated and reserved in complex-valued form, namely, magnitude information …

Automatic target recognition of synthetic aperture radar (SAR) images has been a vital issue
in recent studies. The recognition methods can be divided into two main types: traditional …

In the context of ship monitoring in the ocean, targets are usually sparsely distributed. Thus,
synthetic aperture radar (SAR) imaging of the whole scene is usually quite redundant and …

Due to the advantages of Synthetic Aperture Radar (SAR), the study of Automatic Target
Recognition (ATR) has become a hot topic. Deep learning, especially in the case of a …