Contrastive Domain Adaptation-Based Sparse SAR Target Classification under Few-Shot Cases

Abstract

Due to the imaging mechanism of synthetic aperture radar (SAR), it is difficult and costly to acquire abundant labeled SAR images. Moreover, a typical matched filtering (MF) based image faces the problems of serious noise, sidelobes, and clutters, which will bring down the accuracy of SAR target classification. Different from the MF-based result, a sparse image shows better quality with less noise and higher image signal-to-noise ratio (SNR). Therefore, theoretically using it for target classification will achieve better performance. In this paper, a novel contrastive domain adaptation (CDA) based sparse SAR target classification method is proposed to solve the problem of insufficient samples. In the proposed method, we firstly construct a sparse SAR image dataset by using the complex image based iterative soft thresholding (BiIST) algorithm. Then, the simulated and real SAR datasets are simultaneously sent into an unsupervised domain adaptation framework to reduce the distribution difference and obtain the reconstructed simulated SAR images for subsequent target classification. Finally, the reconstructed simulated images are manually labeled and fed into a shallow convolutional neural network (CNN) for target classification along with a small number of real sparse SAR images. Since the current definition of the number of small samples is still vague and inconsistent, this paper defines few-shot as less than 20 per class. Experimental results based on MSTAR under standard operating conditions (SOC) and extended operating conditions (EOC) show that the reconstructed simulated SAR dataset makes up for the insufficient information from limited real data. Compared with other typical deep learning methods based on limited samples, our method is able to achieve higher accuracy especially under the conditions of few shots.

Locality Preserving Property Constrained Contrastive Learning for Object Classification in SAR Imagery

“…In addition, several recently proposed self-supervised learning approaches that achieved state-of-the-art results in SAR ATR were selected to be used in comparison experiments. These included three contrastive self-supervised learning models, i.e., the PL method [24], the CDA method [27], and the ConvT method [30]. In addition, four self-supervised learning methods were used, i.e., the TSDF-N method [9], the ICSGF method [10], the SFAS method [42], and the DKTS-N method [43].…”

“…Based on the instance-level contrastive loss, Zhai et al proposed batch instance discrimination and feature clustering (BIDFC), which can adjust the embedding distance in the feature space [26]. A contrastive domain adaption methodology was used by Bi et al to reduce the disparity in distribution between a simulated and actual sparse SAR dataset [27]. The efficiency of multi-view contrastive loss was confirmed by Chen et al [28].…”