A CA-Based Weighted Clustering Adversarial Network for Unsupervised Domain Adaptation PolSAR Image Classification

With the development of science and technology, although more and more polarimetric synthetic aperture radar (PolSAR) data are collected, marking PolSAR data still requires a lot of costs. Moreover, the datasets between different domains have the class distribution shift problem, which reduces the reusability of labeled samples between cross-domain images. To address this issue, this letter proposed an unsupervised domain adaptation (UDA) network based on coordinate attention (CA) and weighted clustering. First, an adversarial UDA network with a biclassifier is introduced to eliminate the problem of class distribution shift and achieve alignment of data distribution between different domains. Second, the CA mechanism is introduced to select important features to enhance the utilization of spatial information among pixels. Finally, to improve the utilization of semantic and classification information of the target domain, and to align same class samples, a weighted clustering algorithm is introduced. Experimental results show that compared with the existing UDA method, the proposed method can achieve better PolSAR image classification.