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Found 17 papers, 2 papers with code
Shadow-Oriented Tracking Method for Multi-Target Tracking in Video-SAR
To avoid missed tracking, a detection method based on deep learning is designed to thoroughly learn shadows' features, thus increasing the accurate estimation.
Solving 3D Radar Imaging Inverse Problems with a Multi-cognition Task-oriented Framework
Current methods obtain undifferentiated results that suffer task-depended information retrieval loss and thus don't meet the task's specific demands well.
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging
To address that, we propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features.
Near-filed SAR Image Restoration with Deep Learning Inverse Technique: A Preliminary Study
Adhering to it, a model-based deep learning network is designed to restore the image.
3D Super-Resolution Imaging Method for Distributed Millimeter-wave Automotive Radar System
In this paper, a new distributed mmW radar system is designed to solve this problem.
AETomo-Net: A Novel Deep Learning Network for Tomographic SAR Imaging Based on Multi-dimensional Features
To address them, in this paper, a novel imaging network (AETomo-Net) based on multi-dimensional features is proposed.
Constant-Time-Delay Interferences In Near-Field SAR: Analysis And Suppression In Image Domain
To bridge this gap, in the first time, analysis and the suppression method of interferences in near-field SAR are presented in this work.
Near-Field SAR Image Restoration Based On Two Dimensional Spatial-Variant Deconvolution
To ease this restriction, in this work an image restoration method based on the 2D spatial-variant deconvolution is proposed.
Complicated Background Suppression of ViSAR Image For Moving Target Shadow Detection
The existing Video Synthetic Aperture Radar (ViSAR) moving target shadow detection methods based on deep neural networks mostly generate numerous false alarms and missing detections, because of the foreground-background indistinguishability.
Two Dimensional Sparse-Regularization-Based InSAR Imaging with Back-Projection Embedding
First, to enhance the imaging quality, we propose a new imaging framework base on 2D sparse regularization, where the characteristic of scene is embedded.
Sar Ship Detection based on Swin Transformer and Feature Enhancement Feature Pyramid Network
Swin Transformer serves as backbone to model long-range dependencies and generates hierarchical features maps.
A Dual-Polarization Information Guided Network for SAR Ship Classification
How to fully utilize polarization to enhance synthetic aperture radar (SAR) ship classification remains an unresolved issue.
A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation
Most of existing synthetic aperture radar (SAR) ship in-stance segmentation models do not achieve mask interac-tion or offer limited interaction performance.
Shadow-Background-Noise 3D Spatial Decomposition Using Sparse Low-Rank Gaussian Properties for Video-SAR Moving Target Shadow Enhancement
Moving target shadows among video synthetic aperture radar (Video-SAR) images are always interfered by low scattering backgrounds and cluttered noises, causing poor detec-tion-tracking accuracy.
Multi-View Imputation and Cross-Attention Network Based on Incomplete Longitudinal and Multimodal Data for Conversion Prediction of Mild Cognitive Impairment
To address these challenges, a multi-view imputation and cross-attention network (MCNet) was proposed to integrate data imputation and MCI conversion prediction in a unified framework.
Balance Scene Learning Mechanism for Offshore and Inshore Ship Detection in SAR Images
Huge imbalance of different scenes' sample numbers seriously reduces Synthetic Aperture Radar (SAR) ship detection accuracy.
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
