Search Results for author:
Found 12 papers, 8 papers with code
$\mathbfΦ$-GAN: Physics-Inspired GAN for Generating SAR Images Under Limited Data
We propose two physical loss functions: one for the generator, guiding it to produce SAR images with physical parameters consistent with real ones, and one for the discriminator, enhancing its robustness by basing decisions on PSC attributes.
PolSAM: Polarimetric Scattering Mechanism Informed Segment Anything Model
PolSAR data presents unique challenges due to its rich and complex characteristics.
Physics-Guided Detector for SAR Airplanes
The main contributions of PGD include the physics-guided self-supervised learning, feature enhancement, and instance perception, denoted as PGSSL, PGFE, and PGIP, respectively.
Ranked #1 on
Object Detection
on SAR-AIRcraft-1.0
(using extra training data)
Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey
To our best knowledge, this survey is the first exhaustive examination of the interdiscipline of SAR and GenAI, encompassing a wide range of topics, including deep neural networks, physical models, computer vision, and SAR images.
Guidance Disentanglement Network for Optics-Guided Thermal UAV Image Super-Resolution
However, single guidance models make it difficult to generate effective guidance features under favorable and adverse conditions in UAV scenarios, thus limiting the performance of OTUAV-SR. To address this issue, we propose a novel Guidance Disentanglement network (GDNet), which disentangles the optical image representation according to typical UAV scenario attributes to form guidance features under both favorable and adverse conditions, for robust OTUAV-SR.
X-Fake: Juggling Utility Evaluation and Explanation of Simulated SAR Images
The distribution inconsistency between real and simulated data is the main obstacle that influences the utility of simulated SAR images.
Interpretable attributed scattering center extracted via deep unfolding
Most existing sparse representation-based approaches for attributed scattering center (ASC) extraction adopt traditional iterative optimization algorithms, which suffer from lengthy computation times and limited precision.
Physics Inspired Hybrid Attention for SAR Target Recognition
There has been a recent emphasis on integrating physical models and deep neural networks (DNNs) for SAR target recognition, to improve performance and achieve a higher level of physical interpretability.
Explainable, Physics Aware, Trustworthy AI Paradigm Shift for Synthetic Aperture Radar
The recognition or understanding of the scenes observed with a SAR system requires a broader range of cues, beyond the spatial context.
Physically Explainable CNN for SAR Image Classification
In this paper, we first propose a novel physically explainable convolutional neural network for SAR image classification, namely physics guided and injected learning (PGIL).
Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning
The classification of large-scale high-resolution SAR land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging parameters or regional target area differences, and complex scattering mechanisms being different from optical imaging.
What, Where and How to Transfer in SAR Target Recognition Based on Deep CNNs
Based on the analysis, a transitive transfer method via multi-source data with domain adaptation is proposed in this paper to decrease the discrepancy between the source data and SAR targets.
