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.

本文提出了一种基于异质网络对比学习方法(HCLNet)，通过多特征和超像素对未标记的PolSAR数据进行高层表示学习，以实现少样本分类。实验表明，与现有方法相比，HCLNet在三个广泛使用的PolSAR基准数据集上具有优势，并通过消融研究验证了每个组件的重要性。此外，该工作对于如何高效利用PolSAR数据的多特征学习更好的高层表示以及如何构建更适合PolSAR数据的网络具有重要意义。

基于异构网络的对比学习方法用于极化合成孔径雷达土地覆盖分类