| Summary: | Polarimetric synthetic aperture radar (PolSAR) image classification has achieved great progress, but there still exist some obstacles. On the one hand, a large amount of PolSAR data is captured. Nevertheless, most of them are not labeled with land cover categories, which cannot be fully utilized. On the other hand, annotating PolSAR images relies more on domain knowledge and manpower, which makes pixel-level annotation harder. To alleviate the above problems, by integrating contrastive learning and transformer, we propose a novel patch-level PolSAR image classification, i.e., two-staged contrastive learning and sub-patch attention based network (TCSPANet). Firstly, the two-staged contrastive learning based network (TCNet) is designed for learning the representation information of PolSAR images without supervision, and obtaining the discrimination and comparability for actual land covers. Then, resorting to transformer, we construct the sub-patch attention encoder (SPAE) for modelling the context within patch samples. For training the TCSPANet, two patch-level datasets are built up based on unsupervised and semi-supervised methods. When predicting, the classification algorithm, classifying or splitting, is put forward to realise non-overlapping and coarse-to-fine patch-level classification. The classification results of multi-PolSAR images with one trained model suggests that our proposed model is superior to the compared methods.
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