3-D SAR data-driven imaging via learned low-rank and sparse priors
In the research topic of three-dimensional (3-D) synthetic aperture radar (SAR) imaging, the
sparsity-enforcing techniques offer promise in shortening the sensing time and improving …
sparsity-enforcing techniques offer promise in shortening the sensing time and improving …
3-D SAR Imaging via Perceptual Learning Framework With Adaptive Sparse Prior
Mathematically, 3-D synthetic aperture radar (SAR) imaging is a typical inverse problem,
which, by nature, can be solved by applying the theory of sparse signal recovery. However …
which, by nature, can be solved by applying the theory of sparse signal recovery. However …
CTV-Net: Complex-valued TV-driven network with nested topology for 3-D SAR imaging
The regularization-based approaches offer promise in improving synthetic aperture radar
(SAR) imaging quality while reducing system complexity. However, the widely applied …
(SAR) imaging quality while reducing system complexity. However, the widely applied …
[HTML][HTML] Deep Learning-Based Approximated Observation Sparse SAR Imaging via Complex-Valued Convolutional Neural Network
Z Ji, L Li, H Bi - Remote Sensing, 2024 - mdpi.com
Sparse synthetic aperture radar (SAR) imaging has demonstrated excellent potential in
image quality improvement and data compression. However, conventional observation …
image quality improvement and data compression. However, conventional observation …
3D SAR imaging method based on learned sparse prior
W Mou, WEI Shunjun, S Rong, Z Zichen, SHI Jun… - 雷达学报, 2022 - radars.ac.cn
The development of 3D Synthetic Aperture Radar (SAR) imaging is currently hampered by
issues such as high data dimension, high system complexity, and low imaging processing …
issues such as high data dimension, high system complexity, and low imaging processing …
TPSSI-Net: Fast and enhanced two-path iterative network for 3D SAR sparse imaging
The emerging field of combining compressed sensing (CS) and three-dimensional synthetic
aperture radar (3D SAR) imaging has shown significant potential to reduce sampling rate …
aperture radar (3D SAR) imaging has shown significant potential to reduce sampling rate …
SAR Non-Sparse Scene Reconstruction Network via Image Feature Representation Learning
J Yang, H Zuo, H An, R Jiang, Z Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) is widely used in various fields due to its all-weather and all-
day working characteristics. With the increasing use of SAR on small platforms, SAR is …
day working characteristics. With the increasing use of SAR on small platforms, SAR is …
SR-ISTA-Net: Sparse representation-based deep learning approach for SAR imaging
H Zhang, J Ni, S Xiong, Y Luo… - IEEE Geoscience and …, 2022 - ieeexplore.ieee.org
Compressed sensing (CS) reconstruction of nonsparse scenes is one of the difficulties in
synthetic aperture radar (SAR) imaging technology. Although the conventional CS method …
synthetic aperture radar (SAR) imaging technology. Although the conventional CS method …
Enhanced compressed sensing 3D SAR imaging via cross-modality EO-SAR joint-sparsity priors
A Rajagopal, J Hilton, D Boutte… - … Radar Imagery XXX, 2023 - spiedigitallibrary.org
We introduce a compressed sensing technique for leveraging prior electro-optic (EO)
imagery to improve 3D synthetic aperture radar (SAR) imaging performance. Specifically, we …
imagery to improve 3D synthetic aperture radar (SAR) imaging performance. Specifically, we …
LRSR-ADMM-Net: A joint low-rank and sparse recovery network for SAR imaging
H An, R Jiang, J Wu, KC Teh, Z Sun… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) imaging with sub-Nyquist sampled echo is a challenging
task. Compressed sensing (CS) has been widely applied in this case to reconstruct the …
task. Compressed sensing (CS) has been widely applied in this case to reconstruct the …