A comprehensive survey on SAR ATR in deep-learning era
J Li, Z Yu, L Yu, P Cheng, J Chen, C Chi - Remote Sensing, 2023 - mdpi.com
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 …
Recognition (ATR) has become a hot topic. Deep learning, especially in the case of a …
Beyond supervised learning in remote sensing: A systematic review of deep learning approaches
An increasing availability of remote sensing data in the era of geo big-data makes producing
well-represented, reliable training data to be more challenging and requires an excessive …
well-represented, reliable training data to be more challenging and requires an excessive …
Domain knowledge powered two-stream deep network for few-shot SAR vehicle recognition
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 …
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 …
Few-shot class-incremental SAR target recognition based on hierarchical embedding and incremental evolutionary network
It is difficult to realize effective synthetic aperture radar (SAR) automatic target recognition
(ATR) in open scenarios because the ATR model cannot continuously learn from new …
(ATR) in open scenarios because the ATR model cannot continuously learn from new …
SAR automatic target recognition method based on multi-stream complex-valued networks
In synthetic aperture radar automatic target recognition (SAR-ATR), target information is
usually propagated and reserved in complex-valued form, namely, magnitude information …
usually propagated and reserved in complex-valued form, namely, magnitude information …
[HTML][HTML] Physically explainable CNN for SAR image classification
Integrating the special electromagnetic characteristics of Synthetic Aperture Radar (SAR) in
deep neural networks is essential in order to enhance the explainability and physics …
deep neural networks is essential in order to enhance the explainability and physics …
Cross-task and cross-domain SAR target recognition: A meta-transfer learning approach
Meta learning and transfer learning offer promising solutions to the problem of requiring
large amounts of data in deep learning approaches for synthetic aperture radar (SAR) target …
large amounts of data in deep learning approaches for synthetic aperture radar (SAR) target …
Meta-learning based hyperspectral target detection using Siamese network
Y Wang, X Chen, F Wang, M Song… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
When predicting data for which limited supervised information is available, hyperspectral
target detection methods based on deep transfer learning expect that the network will not …
target detection methods based on deep transfer learning expect that the network will not …
Multilevel scattering center and deep feature fusion learning framework for SAR target recognition
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 …
types of methods: the physics-driven model and the data-driven network. The physics-driven …