Unsupervised Difference-Guided Adaptive Fusion Network for Change Detection in PolSAR Images

It is challenging to accurately extract change features in polarimetric synthetic aperture radar (PolSAR) image change detection. Recently, many existing convolutional neural network (CNN)-based change detection algorithms show satisfactory performance, but error detection still exists in the acquired change features. In this article, we propose an unsupervised difference-guided adaptive fusion network (DGAFN), which contains three key modules, i.e., adaptive difference extraction module (ADEM), difference feature enhancement module (DFEM), and adaptive weighted fusion module (AWFM). First, ADEM extracts initial multiscale difference features through nonlocal attention mechanisms guided by bitemporal feature differences, which helps the network to capture the correlation and difference between bitemporal features more accurately. Second, in order to further refine multiscale difference features and reduce pseudo-changes, DFEM uses class activation maps (CAMs) and contextual feature extraction modules (CFEMs) to perceive more polarization contextual information in a larger scale range. Finally, in order to improve the recognition ability of change targets with different sizes, AWFM adaptively fuses enhanced multiscale difference features in spatial and channel dimensions by introducing attention mechanisms to obtain change features with multiscale complementary information. Comparative experimental results on five measured PolSAR datasets obtained by GaoFen-3 show that DGAFN is superior to other relevant advanced algorithms in change detection performance.