Scattering-Point Topology for Few-Shot Ship Classification in SAR Images

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 challenging due to the limited availability of labeled SAR data, complex radar backscatter, and variations in imaging parameters. In this article, 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 through a series of operations to encode the topological information of scattering points, resulting in an SPT embedding that improves the network's adaptability to the imaging mechanism and reduces imaging variability in SAR images. To effectively fuse the SPT embedding and image features extracted from a convolutional neural network, we introduce a novel mechanism named reciprocal feature fusion attention. In addition, to address the limited diversity in the training data, we apply fine-tuning-based 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 the existing methods in this domain.

Keywords

• Few-shot learning (FSL)
• scattering point
• scattering topology
• ship classification
• synthetic aperture radar (SAR)