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 …
Automatic target recognition on synthetic aperture radar imagery: A survey
Automatic target recognition (ATR) for military applications is one of the core processes
toward enhancing intelligence and autonomously operating military platforms. Spurred by …
toward enhancing intelligence and autonomously operating military platforms. Spurred by …
Domain knowledge powered two-stream deep network for few-shot SAR vehicle recognition
L Zhang, X Leng, S Feng, X Ma, K Ji… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
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 …
labeled data. Although few-shot learning methods are developed to extract more information …
Multi-scale ship detection algorithm based on a lightweight neural network for spaceborne SAR images
The current limited spaceborne hardware resources and the diversity of ship target scales in
SAR images have led to the requirement of on-orbit real-time detection of ship targets in …
SAR images have led to the requirement of on-orbit real-time detection of ship targets in …
Mixed loss graph attention network for few-shot SAR target classification
Restricted by the observation condition, synthetic aperture radar (SAR) automatic target
classification based on deep learning usually suffers from insufficient training samples. To …
classification based on deep learning usually suffers from insufficient training samples. To …
SAR target classification based on integration of ASC parts model and deep learning algorithm
S Feng, K Ji, L Zhang, X Ma… - IEEE Journal of Selected …, 2021 - ieeexplore.ieee.org
Automatic target recognition of synthetic aperture radar (SAR) images has been a vital issue
in recent studies. The recognition methods can be divided into two main types: traditional …
in recent studies. The recognition methods can be divided into two main types: traditional …
Rotation awareness based self-supervised learning for SAR target recognition with limited training samples
Z Wen, Z Liu, S Zhang, Q Pan - IEEE Transactions on Image …, 2021 - ieeexplore.ieee.org
The scattering signatures of a synthetic aperture radar (SAR) target image will be highly
sensitive to different azimuth angles/poses, which aggravates the demand for training …
sensitive to different azimuth angles/poses, which aggravates the demand for training …
Multiscale representation learning for image classification: A survey
Feature representation has been widely used and developed recently. Multiscale features
have led to remarkable breakthroughs for representation learning process in many computer …
have led to remarkable breakthroughs for representation learning process in many computer …
HENC: Hierarchical embedding network with center calibration for few-shot fine-grained SAR target classification
Restricted by observation conditions, some scarce targets in the synthetic aperture radar
(SAR) image only have a few samples, making effective classification a challenging task …
(SAR) image only have a few samples, making effective classification a challenging task …
EFTL: Complex convolutional networks with electromagnetic feature transfer learning for SAR target recognition
Considering that synthetic aperture radar (SAR) images obtained directly after signal
processing are in the form of complex matrices, we propose a complex convolutional …
processing are in the form of complex matrices, we propose a complex convolutional …