Statistic-Guided Difference Enhancement Graph Transformer for Unsupervised Change Detection in PolSAR Images

Polarimetric synthetic aperture radar (PolSAR) image change detection (CD) aims to accurately analyze the difference and detect changes in PolSAR images. Recently, graph transformer (GT), which combines the advantages of graph convolutional network and transformer, has increasingly attracted attention in the field of remote sensing. However, the direct application of GT for PolSAR image CD with limited training samples is challenging owing to polarimetric scattering confusion and random speckle noise. Here, we propose a novel unsupervised representation learning framework for CD in PolSAR images, named statistic-guided difference enhancement GT (SDEGT). Our motivation is that polarimetric statistics can effectively guide GT to extract robust and highly discriminative features from the raw polarimetric graphs and thus accurately detect changes. The SDEGT follows the architecture based on neighborhood aggregation GT and innovatively introduces polarimetric statistics to guide feature difference enhancement, thereby capturing the structural interaction between graph nodes and aggregating the local-to-global change correlations at low computational cost. First, SDEGT innovatively introduces noise-robust polarimetric statistics to improve its noise suppression ability and learn sufficient change-aware features from the PolSAR data. Subsequently, guided by the polarimetric statistical difference, a difference enhancement module (DEM) is designed and cleverly embedded in the SDEGT to adaptively enhance the difference between changed and unchanged nodes, thus improving the discrimination of the change-aware features. Finally, symmetric cross-entropy (SCE) is employed to facilitate the robust learning of SDEGT and attenuate the detrimental effect of label noise. Visual and quantitative experimental results on five measured PolSAR datasets with different scenes and dimensions demonstrate the competitiveness of our SDEGT over other state-of-the-art methods.