Unsupervised Cross-Domain Network Based on Bi-classifier for PolSAR Image Classification

Polarimetric Synthetic Aperture Radar (PolSAR) image classification is an important research content in the field of remote sensing. However, the lack of effective labeled data and the serious class distribution shift problem between different datasets hinder the application of deep learning networks in PolSAR image classification. Therefore, this paper proposes a Bi-classifier adversarial network based on covariance difference matrix and Convolutional Block Attention Module (CBAM) to solve the cross-domain problem in PolSAR images. Firstly, since PolSAR image processing generally uses the covariance matrix or coherence matrix of a single target pixel to construct the data set, which cannot effectively use the local information of PolSAR data,Therefore, the PolSAR coherence difference matrix P is proposed as the description of the PolSAR image difference information. Secondly, due to the different importance of features between neighboring pixels, the CBAM mechanism is introduced in order to exploit the spatial information of PolSAR data. Finally, in order to solve the cross-domain problem of PolSAR images more effectively, a bi-classifier adversarial network based on covariance difference matrix and CBAM is proposed. In this paper, we conduct experiments on public datasets, using images of San FranciscoⅠ acquired in 2008 as the source domain, and images of San Francisco Ⅱ acquired in 1992 as the target domain data. The experimental results show that the proposed method can effectively solve the cross-domain problem of PolSAR images.