The purpose of this paper is to survey and assess the state-of-the-art in automatic target
recognition for synthetic aperture radar imagery (SAR-ATR). The aim is not to develop an …

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 …

The active recognition of interesting targets has been a vital issue for synthetic aperture
radar (SAR) systems. The SAR recognition methods are mainly grouped as follows …

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 …

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

We develop a method for the formation of spotlight-mode synthetic aperture radar (SAR)
images with enhanced features. The approach is based on a regularized reconstruction of …

The recognition or understanding of the scenes observed with a synthetic aperture radar
(SAR) system requires a broader range of cues beyond the spatial context. These …

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 …

We present a parametric model for radar scattering as a function of frequency and aspect
angle. The model is used for analysis of synthetic aperture radar measurements. The …