  Polarimetric synthetic aperture radar (PolSAR) image interpretation is widely
used in various fields. Recently, deep learning has made significant progress
in PolSAR image classification. Supervised learning (SL) requires a large
amount of labeled PolSAR data with high quality to achieve better performance,
however, manually labeled data is insufficient. This causes the SL to fail into
overfitting and degrades its generalization performance. Furthermore, the
scattering confusion problem is also a significant challenge that attracts more
attention. To solve these problems, this article proposes a Heterogeneous
Network based Contrastive Learning method(HCLNet). It aims to learn high-level
representation from unlabeled PolSAR data for few-shot classification according
to multi-features and superpixels. Beyond the conventional CL, HCLNet
introduces the heterogeneous architecture for the first time to utilize
heterogeneous PolSAR features better. And it develops two easy-to-use plugins
to narrow the domain gap between optics and PolSAR, including feature filter
and superpixel-based instance discrimination, which the former is used to
enhance the complementarity of multi-features, and the latter is used to
increase the diversity of negative samples. Experiments demonstrate the
superiority of HCLNet on three widely used PolSAR benchmark datasets compared
with state-of-the-art methods. Ablation studies also verify the importance of
each component. Besides, this work has implications for how to efficiently
utilize the multi-features of PolSAR data to learn better high-level
representation in CL and how to construct networks suitable for PolSAR data
better.
