Automatic target recognition in synthetic aperture radar imagery: A state-of-the-art review
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 …
recognition for synthetic aperture radar imagery (SAR-ATR). The aim is not to develop an …
A review of the autoencoder and its variants: A comparative perspective from target recognition in synthetic-aperture radar images
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 …
become a hot new topic for research [1]-[4]. The revival of interest in such deep networks can …
HOG-ShipCLSNet: A novel deep learning network with hog feature fusion for SAR ship classification
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 …
step in ocean surveillance. Recently, with the rise of deep learning (DL), modern abstract …
Target classification using the deep convolutional networks for SAR images
The algorithm of synthetic aperture radar automatic target recognition (SAR-ATR) is
generally composed of the extraction of a set of features that transform the raw input into a …
generally composed of the extraction of a set of features that transform the raw input into a …
Convolutional neural network with data augmentation for SAR target recognition
Many methods have been proposed to improve the performance of synthetic aperture radar
(SAR) target recognition but seldom consider the issues in real-world recognition systems …
(SAR) target recognition but seldom consider the issues in real-world recognition systems …
SAR automatic target recognition based on multiview deep learning framework
J Pei, Y Huang, W Huo, Y Zhang… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
It is a feasible and promising way to utilize deep neural networks to learn and extract
valuable features from synthetic aperture radar (SAR) images for SAR automatic target …
valuable features from synthetic aperture radar (SAR) images for SAR automatic target …
Improving SAR automatic target recognition models with transfer learning from simulated data
Data-driven classification algorithms have proved to do well for automatic target recognition
(ATR) in synthetic aperture radar (SAR) data. Collecting data sets suitable for these …
(ATR) in synthetic aperture radar (SAR) data. Collecting data sets suitable for these …
Hybrid inference network for few-shot SAR automatic target recognition
Synthetic aperture radar (SAR) automatic target recognition (ATR) plays an important role in
SAR image interpretation. However, at least hundreds of training samples are usually …
SAR image interpretation. However, at least hundreds of training samples are usually …
SAR target classification using the multikernel-size feature fusion-based convolutional neural network
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 …
synthetic aperture radar (SAR) target classification. In the convolutional layer of CNN …
SCAN: Scattering characteristics analysis network for few-shot aircraft classification in high-resolution SAR images
Recently, deep learning in synthetic aperture radar (SAR) automatic target recognition (ATR)
has made significant progress, but the sample limitation problem in the SAR field is still …
has made significant progress, but the sample limitation problem in the SAR field is still …