One remarkable trend in applying synthetic aperture radar technology is the automatic
interpretation of Synthetic Aperture Radar (SAR) images. The electromagnetic scattering …

合成孔径雷达(SAR) 图像的自动化解译是合成孔径雷达技术应用的重要发展方向之一.
电磁散射特征与目标结构具有稳健的关联性, 是SAR 图像解译的关键支撑. 近年来 …

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 …

The recognition or understanding of the scenes observed with a synthetic aperture radar
(SAR) system requires a broader range of cues beyond the spatial context. These …

Deep learning has offered new ideas in SAR ship target recognition. Although many
methods improve the recognition performance through the improvement of loss function and …

Machine learning methods for synthetic aperture radar (SAR) image automatic target
recognition (ATR) can be divided into two main types: traditional methods and deep learning …

Generative adversarial networks (or GANs) are a specific deep learning architecture often
used for different usages, such as data generation or image-to-image translation. In recent …

Deep learning is widely applied in synthetic aperture radar (SAR) target recognition.
However, the high cost of collecting real SAR target data leads to insufficient data volume …

Detecting ships in synthetic aperture radar (SAR) images poses a formidable challenge,
primarily attributed to limited observation samples and complex environments. To address …

Deep learning-based methods have demonstrated exceptional performance in the field of
synthetic aperture radar automatic target recognition (SAR ATR). However, obtaining a …