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Found 11 papers, 1 papers with code
Skeleton Supervised Airway Segmentation
Then, we construct a geometry-aware dual-path propagation framework (GDP) to further promote complementary propagation learning, composed of hard geometry-aware propagation learning and soft geometry-aware propagation guidance.
A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography
This paper presents an innovative gridless 3-D imaging framework tailored for UAV-borne TomoSAR.
Conceptual Study and Performance Analysis of Tandem Dual-Antenna Spaceborne SAR Interferometry
In this article, a new concept of Tandem Dual-Antenna SAR Interferometry (TDA-InSAR) system for single-pass reliable 3D surface mapping using the asymptotic 3D PU is proposed.
CVGG-Net: Ship Recognition for SAR Images Based on Complex-Valued Convolutional Neural Network
Although convolutional neural networks have been successfully employed for SAR image target recognition, surpassing traditional algorithms, most existing research concentrates on the amplitude domain and neglects the essential phase information.
SPHR-SAR-Net: Superpixel High-resolution SAR Imaging Network Based on Nonlocal Total Variation
High-resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties.
GDDS: Pulmonary Bronchioles Segmentation with Group Deep Dense Supervision
To address these problems, we contribute a new bronchial segmentation method based on Group Deep Dense Supervision (GDDS) that emphasizes fine-scale bronchioles segmentation in a simple-but-effective manner.
ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold
In addition, adaptive threshold is introduced for each azimuth-range pixel, enabling the threshold shrinkage to be not only layer-varied but also element-wise.
TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network
Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations.
A Novel Gradient Descent Least Squares (GDLS) Algorithm for Efficient SMV Gridless Line Spectrum Estimation with Applications in Tomographic SAR Imaging
Numerical simulations and real data experiments show that the proposed GDLS algorithm outperforms the state-of-the-art methods e. g., CS and ANM, in terms of estimation performances.
SAR Image Classification Based on Spiking Neural Network through Spike-Time Dependent Plasticity and Gradient Descent
Based on the TensorFlow framework, a single layer supervised learning SNN is built from the bottom, and the classification accuracy reaches 90. 05\%.
