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 …

A comprehensive and well-structured review on the application of deep learning (DL) based
algorithms, such as convolutional neural networks (CNN) and long-short term memory …

The publicly-available Moving and Stationary Target Acquisition and Recognition (MSTAR)
synthetic aperture radar (SAR) dataset has been an valuable tool in the development of SAR …

Obtaining measured synthetic aperture radar (SAR) data for training automatic target
recognition (ATR) models can be too expensive (in terms of time and money) and complex …

Synthetic aperture radar (SAR) image target recognition has consistently been a research
hotspot in the field of radar image interpretation. Compared with traditional target recognition …

Deep-learning-based models usually require a large amount of data for training, which
guarantees the effectiveness of the trained model. Generative models are no exception, and …

Synthetic aperture radar (SAR) is an imaging modality used for a variety of military and
civilian tasks, many of which could benefit greatly from computer automation. The increase …

Convolutional neural networks (CNN) are tremendously successful at classifying objects in
electro-optical images. However, with synthetic aperture radar (SAR) data, off-the-shelf …

Abstract Synthetic Aperture Radar (SAR) target classification methods based on
convolutional neural networks have attracted wide attention recently. Typically, these …

Because of the high cost of data acquisition in synthetic aperture radar (SAR) target
recognition, the application of synthetic (simulated) SAR data is becoming increasingly …