Synthetic Aperture Radar image classification based on constrictive learning with limited data

It is difficult to obtain a large number of high-quality labelled Synthetic Aperture Radar (SAR) image data. In order to solve the classification task of an SAR image with limited data, this paper proposes a dual-network classification model of pre-training and fine-tuning based on contrastive learning and pseudo-label training strategy. The algorithm firstly obtains the initial weight of the network through unsupervised pre-training and then takes advantage of the pseudo-label information to realise the fine tuning of the dual network. It effectively reduces the number of samples required during classification. Compared with the classification network without optimisation strategy, the proposed algorithm achieves higher classification accuracy in the ablation study.