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 …

Ship classification in synthetic aperture radar (SAR) images is a fundamental and significant
step in ocean surveillance. Recently, with the rise of deep learning (DL), modern abstract …

Current synthetic aperture radar (SAR) ship classifiers using convolutional neural networks
(CNNs) offer state-of-the-art performance. Yet, they still have two defects potentially …

Traditionally, nonlinear data processing has been approached via the use of polynomial
filters, which are straightforward expansions of many linear methods, or through the use of …

This letter proposes a squeeze-and-excitation Laplacian pyramid network with dual-
polarization feature fusion (SE-LPN-DPFF) for ship classification in synthetic aperture radar …

It is well-known that the convolutional neural network (CNN) is an effective method for
synthetic aperture radar (SAR) target classification. In the convolutional layer of CNN …

Restricted by the observation condition, synthetic aperture radar (SAR) automatic target
classification based on deep learning usually suffers from insufficient training samples. To …

Convolutional neural networks (CNNs) have dominated the synthetic aperture radar (SAR)
automatic target recognition (ATR) for years. However, under limited SAR images, the width …

In synthetic aperture radar (SAR) automatic target recognition (ATR), there are mainly two
types of methods: the physics-driven model and the data-driven network. The physics-driven …

Maritime surveillance is indispensable for civilian fields, including national maritime
safeguarding, channel monitoring, and so on, in which synthetic aperture radar (SAR) ship …