CDFNet: Cross-Domain Feature Fusion Network for PolSAR Terrain Classification

The scarcity of labeled data and domain shift among polarimetric synthetic aperture radar (PolSAR) images degrades the performance of the supervised-learning-based algorithm. Some unsupervised domain adaptation (UDA) algorithms have been proposed to address this problem and achieve good performance. The existing UDA algorithms for PolSAR terrain classification focus on the feature distribution shift problem but ignore the label shift problem in UDA task. In addition, feature alignment-based algorithms generate pseudo labels for target domain which introduce label noise and compromising the UDA performance. To alleviate the problems above, we present a cross-domain feature fusion network (CDFNet) for PolSAR terrain classification. Specifically, a domain-balanced sampling (DBS) module is proposed to obtain a nearly balanced training dataset to alleviate the label shift problem. Then, a cross-domain feature fusion (CDF) module is presented to achieve class-wise feature alignment with no additional label noise introduction. Experimental results on four PolSAR datasets demonstrate that our algorithm outperforms state-of-the-art UDA algorithms in terms of target domain performance.