Hand gesture recognition via radar sensors and convolutional neural networks
In this communication, a low-cost radar-sensor-based apparatus for contactless hand
gesture recognition via Doppler signature analysis is proposed. The raw reflected signal …
gesture recognition via Doppler signature analysis is proposed. The raw reflected signal …
γ-Net: Superresolving SAR tomographic inversion via deep learning
Synthetic aperture radar tomography (TomoSAR) has been extensively employed in 3-D
reconstruction in dense urban areas using high-resolution SAR acquisitions. Compressive …
reconstruction in dense urban areas using high-resolution SAR acquisitions. Compressive …
MAda-Net: Model-adaptive deep learning imaging for SAR tomography
Y Wang, C Liu, R Zhu, M Liu, Z Ding… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The compressive sensing (CS)-based tomographic SAR (TomoSAR) 3-D imaging method
has the shortcoming of low efficiency, mainly represented in two aspects: first, the CS solver …
has the shortcoming of low efficiency, mainly represented in two aspects: first, the CS solver …
Super-resolution for MIMO array SAR 3-D imaging based on compressive sensing and deep neural network
C Wu, Z Zhang, L Chen, W Yu - IEEE Journal of Selected …, 2020 - ieeexplore.ieee.org
Multiple-input multiple-output (MIMO) array synthetic aperture radar (SAR) can straightly
obtain the 3-D imagery of the illuminated scene with the single-pass flight. Generally, the …
obtain the 3-D imagery of the illuminated scene with the single-pass flight. Generally, the …
MEG source localization via deep learning
D Pantazis, A Adler - Sensors, 2021 - mdpi.com
We present a deep learning solution to the problem of localization of
magnetoencephalography (MEG) brain signals. The proposed deep model architectures are …
magnetoencephalography (MEG) brain signals. The proposed deep model architectures are …
A deep learning solution for height estimation on a forested area based on Pol-TomoSAR data
Forest height and underlying terrain reconstruction is one of the main aims in dealing with
forested areas. Theoretically, synthetic aperture radar tomography (TomoSAR) offers the …
forested areas. Theoretically, synthetic aperture radar tomography (TomoSAR) offers the …
MLC-net: A sparse reconstruction network for TomoSAR imaging based on multi-label classification neural network
D Ouyang, Y Zhang, J Guo, G Zhou - ISPRS Journal of Photogrammetry and …, 2025 - Elsevier
Abstract Synthetic Aperture Radar tomography (TomoSAR) has garnered significant interest
for its capability to achieve three-dimensional resolution along the elevation angle by …
for its capability to achieve three-dimensional resolution along the elevation angle by …
[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 …
Innovative Rotating SAR Mode for 3D Imaging of Buildings
Three-dimensional SAR imaging of urban buildings is currently a hotspot in the research
area of remote sensing. Synthetic Aperture Radar (SAR) offers all-time, all-weather, high …
area of remote sensing. Synthetic Aperture Radar (SAR) offers all-time, all-weather, high …
Super-resolving sar tomography using deep learning
Synthetic aperture radar tomography (TomoSAR) has been widely employed in 3-D urban
mapping. However, state-of-the-art super-resolving TomoSAR algorithms are …
mapping. However, state-of-the-art super-resolving TomoSAR algorithms are …