Search Results for author:
Found 5 papers, 4 papers with code
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.
