Lack of labeled data is a common problem among synthetic aperture radar (SAR) target
recognition, which can be defined as few-shot and limited-data SAR target recognition. The …

Synthetic aperture radar (SAR) targets often exhibit characteristics, such as high mobility
and strong concealment, resulting in scarce SAR data and the manifestation of few-shot data …

In recent years, deep learning (DL) networks have achieved considerable success in the
SAR target detection task. Here, current DL-based methods, originally designed for natural …

When new unknown samples are captured continually in the open-world environment, the
concept diversity accumulation of existing classes and the identification/creation of new …

The rise of deep learning has furnished a potent boost for the rapid development of
automatic target recognition (ATR) in synthetic aperture radar (SAR) imagery. Existing SAR …

Mainstream DNN-based SAR-ATR methods still face issues such as easy overfitting of a few
training data, high computational overhead, and poor interpretability of the black-box model …