In recent years, unsupervised feature learning based on a neural network architecture has
become a hot new topic for research [1]-[4]. The revival of interest in such deep networks can …

Automatic target recognition (ATR) for military applications is one of the core processes
toward enhancing intelligence and autonomously operating military platforms. Spurred by …

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 …

合成孔径雷达(SAR) 图像的自动化解译是合成孔径雷达技术应用的重要发展方向之一.
电磁散射特征与目标结构具有稳健的关联性, 是SAR 图像解译的关键支撑. 近年来 …

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 …

The range resolution of high-resolution wideband radar is much smaller than the target size.
Its echo signals tend to be diverse and sensitive to small changes of targets. Therefore, it is …

Deep learning has obtained state-of-the-art results in a variety of computer vision tasks and
has also been used to solve SAR image classification problems. Deep learning algorithms …

This paper proposes a robust synthetic aperture radar (SAR) automatic target recognition
method based on the 3-D scattering center model. The 3-D scattering center model is …

Although the deep convolutional neural network (DCNN) has been successfully applied to
automatic target recognition (ATR) of ground vehicles based on synthetic aperture radar …