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 …
A survey on the applications of convolutional neural networks for synthetic aperture radar: Recent advances
In recent years, convolutional neural networks (CNNs) have drawn considerable attention
for the analysis of synthetic aperture radar (SAR) data. In this study, major subareas of SAR …
for the analysis of synthetic aperture radar (SAR) data. In this study, major subareas of SAR …
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 …
Deep learning meets SAR: Concepts, models, pitfalls, and perspectives
Deep learning in remote sensing has received considerable international hype, but it is
mostly limited to the evaluation of optical data. Although deep learning has been introduced …
mostly limited to the evaluation of optical data. Although deep learning has been introduced …
U-net-based semantic classification for flood extent extraction using SAR imagery and GEE platform: A case study for 2019 central US flooding
Data-driven models for water body extraction have experienced accelerated growth in
recent years, thanks to advances in processing techniques and computational resources, as …
recent years, thanks to advances in processing techniques and computational resources, as …
HyperLi-Net: A hyper-light deep learning network for high-accurate and high-speed ship detection from synthetic aperture radar imagery
Abstract Ship detection from Synthetic Aperture Radar (SAR) imagery is attracting increasing
attention due to its great value in ocean. However, existing most studies are frequently …
attention due to its great value in ocean. However, existing most studies are frequently …
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 …
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 …
Squeeze-and-excitation Laplacian pyramid network with dual-polarization feature fusion for ship classification in SAR images
T Zhang, X Zhang - IEEE Geoscience and Remote Sensing …, 2021 - ieeexplore.ieee.org
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 …
polarization feature fusion (SE-LPN-DPFF) for ship classification in synthetic aperture radar …
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 …