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 …

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 convolutional neural networks (CNNs) have been widely used and achieved state-of-
the-art performance in many image or video processing and analysis tasks. In particular, for …

Recently, the application of satellite remote sensing images is becoming increasingly
popular, but the observed images from satellite sensors are frequently in low-resolution …

In synthetic aperture radar (SAR) automatic target recognition, it is expensive and time-
consuming to annotate the targets. Thus, training a network with a few labeled data and …

Synthetic aperture radar (SAR) is a microwave remote sensing system. It has a broad scope
of applications in both military and civilian fields. Benefited from the latest advances in deep …

Radar automatic target recognition (RATR) technology is fundamental but complicated
system engineering that combines sensor, target, environment, and signal processing …

Modern radar systems have high requirements in terms of accuracy, robustness and real-
time capability when operating on increasingly complex electromagnetic environments …

Demand towards the recognition of a target with a specific spatial signature by using
remotely sensed images, the process of discovering the location, pose, and class that …