Research progress on few-shot learning for remote sensing image interpretation
The rapid development of deep learning brings effective solutions for remote sensing image
interpretation. Training deep neural network models usually require a large number of …
interpretation. Training deep neural network models usually require a large number of …
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 …
ASRNN: A recurrent neural network with an attention model for sequence labeling
Natural language processing (NLP) is useful for handling text and speech, and sequence
labeling plays an important role by automatically analyzing a sequence (text) to assign …
labeling plays an important role by automatically analyzing a sequence (text) to assign …
Deep transfer learning for few-shot SAR image classification
The reemergence of Deep Neural Networks (DNNs) has lead to high-performance
supervised learning algorithms for the Electro-Optical (EO) domain classification and …
supervised learning algorithms for the Electro-Optical (EO) domain classification and …
Deep-learning for radar: A survey
Z Geng, H Yan, J Zhang, D Zhu - IEEE Access, 2021 - ieeexplore.ieee.org
A comprehensive and well-structured review on the application of deep learning (DL) based
algorithms, such as convolutional neural networks (CNN) and long-short term memory …
algorithms, such as convolutional neural networks (CNN) and long-short term memory …
Electromagnetic scattering feature (ESF) module embedded network based on ASC model for robust and interpretable SAR ATR
S Feng, K Ji, F Wang, L Zhang, X Ma… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …
aperture radar (SAR) recently. However, most of the studies are based on the network …
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 …
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 …
PAN: Part attention network integrating electromagnetic characteristics for interpretable SAR vehicle target recognition
S Feng, K Ji, F Wang, L Zhang, X Ma… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
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 …
recognition (ATR) can be divided into two main types: traditional methods and deep learning …
SAR-to-optical image translation using supervised cycle-consistent adversarial networks
Optical remote sensing (RS) data suffer from the limitation of bad weather and cloud
contamination, whereas synthetic aperture radar (SAR) can work under all weather …
contamination, whereas synthetic aperture radar (SAR) can work under all weather …