Multi-scale capsule Swin Transformer-based method for SAR image target recognition

A multi-scale capsule Swin Transformer network (MSCSTN) was proposed by synergizing the semantic feature encoding of capsule units with the context feature mapping of Swin Transformer. Capsule encoding and the Swin Transformer were jointly applied to SAR image target recognition. The network was integrated with three parallel capsule Swin Transformer encoding structures, which were fused to classify the input image. Each structure was constructed through a capsule token encoder based on expanded convolutional slice partition and a 3D capsule Swin Transformer module, which designed to capture of more profound and extensive semantic features.The experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset and FUSAR-Ship dataset were shown to demonstrate that MSCSTN outperformed other methods under various test conditions. The results demonstrate that MSCSTN exhibits excellent recognition performance, generalization ability, and potential for application.