Abstract
Synthetic Aperture Radar (SAR) has emerged as a critical technology for
detecting and classifying objects such as ships in challenging
environments. However, few-shot learning remains a challenge due to the
limited availability of labeled SAR data, complex radar backscatter, and
variations in imaging parameters. In this paper, we propose a novel
network, Scattering Point Topology for Few-Shot Ship Classification
(SPT-FSC), which addresses these challenges by incorporating scattering
characteristics into the network learning process through a scattering
point topology (SPT) based on scattering key points. We design a
topology encoding branch (TEB) through a series of operations to encode
the topological information of scattering points, resulting in a
scattering point topology embedding (SPTE) that improves the network’s
adaptability to the imaging mechanism and reduces imaging variability in
SAR images. To effectively fuse the SPTE and image features extracted
from a convolutional neural network (CNN), we introduce a novel
mechanism called reciprocal feature fusion attention (RFFA).
Furthermore, to address the limited diversity in the training data, we
apply transfer learning methodologies and construct a fine-grained ship
classification dataset by combining the OpenSARShip and FUSAR-Ship
datasets. Our comprehensive experiments on these datasets demonstrate
the effectiveness of our proposed SPT-FSC method, achieving high
accuracy and robustness in few-shot ship classification tasks for SAR
images, outperforming existing methods in this domain.