  Deep learning and convolutional neural networks (CNNs) have made progress in
polarimetric synthetic aperture radar (PolSAR) image classification over the
past few years. However, a crucial issue has not been addressed, i.e., the
requirement of CNNs for abundant labeled samples versus the insufficient human
annotations of PolSAR images. It is well-known that following the supervised
learning paradigm may lead to the overfitting of training data, and the lack of
supervision information of PolSAR images undoubtedly aggravates this problem,
which greatly affects the generalization performance of CNN-based classifiers
in large-scale applications. To handle this problem, in this paper, learning
transferrable representations from unlabeled PolSAR data through convolutional
architectures is explored for the first time. Specifically, a PolSAR-tailored
contrastive learning network (PCLNet) is proposed for unsupervised deep PolSAR
representation learning and few-shot classification. Different from the
utilization of optical processing methods, a diversity stimulation mechanism is
constructed to narrow the application gap between optics and PolSAR. Beyond the
conventional supervised methods, PCLNet develops an unsupervised pre-training
phase based on the proxy objective of instance discrimination to learn useful
representations from unlabeled PolSAR data. The acquired representations are
transferred to the downstream task, i.e., few-shot PolSAR classification.
Experiments on two widely-used PolSAR benchmark datasets confirm the validity
of PCLNet. Besides, this work may enlighten how to efficiently utilize the
massive unlabeled PolSAR data to alleviate the greedy demands of CNN-based
methods for human annotations.
