  In recent years, impressive performance of deep learning technology has been
recognized in Synthetic Aperture Radar (SAR) Automatic Target Recognition
(ATR). Since a large amount of annotated data is required in this technique, it
poses a trenchant challenge to the issue of obtaining a high recognition rate
through less labeled data. To overcome this problem, inspired by the
contrastive learning, we proposed a novel framework named Batch Instance
Discrimination and Feature Clustering (BIDFC). In this framework, different
from that of the objective of general contrastive learning methods, embedding
distance between samples should be moderate because of the high similarity
between samples in the SAR images. Consequently, our flexible framework is
equipped with adjustable distance between embedding, which we term as weakly
contrastive learning. Technically, instance labels are assigned to the
unlabeled data in per batch and random augmentation and training are performed
few times on these augmented data. Meanwhile, a novel Dynamic-Weighted Variance
loss (DWV loss) function is also posed to cluster the embedding of enhanced
versions for each sample. Experimental results on the moving and stationary
target acquisition and recognition (MSTAR) database indicate a 91.25%
classification accuracy of our method fine-tuned on only 3.13% training data.
Even though a linear evaluation is performed on the same training data, the
accuracy can still reach 90.13%. We also verified the effectiveness of BIDFC in
OpenSarShip database, indicating that our method can be generalized to other
datasets. Our code is avaliable at:
https://github.com/Wenlve-Zhou/BIDFC-master.
