Abstract: Unsupervised change detection represents a hot research topic in information extraction from synthetic aperture radar (SAR) images, having attracted wide attention in recent years. Although existing studies have made certain advances in this field, available methods for unsupervised change detection primarily focus on the utilization of spatial-domain features, leading to limited explorations into the fusion of spatial-frequency dual-domain features. In this context, this study designed a UNet model for change detection based on Transformer and wavelet transform convolution. Using pseudo-labels obtained by combining log-ratio operation with fuzzy C-means clustering, the UNet model enables the unsupervised information extraction of changes from SAR images. The encoder of the model consists of two branches: spatial- and frequency-domain branches. Among these, the spatial- domain branch extracts global spatial features using Transformer and fuses them with local spatial features extracted using the convolutional network. The frequency-domain branch extracts multi-scale frequency-domain features using wavelet transform convolution, enhancing the model's responses to both low-frequency components carrying the main information of images and high-frequency components carrying boundary details. Finally, the decoder facilitates the organic fusion of multi-scale cross-domain complementary information within the UNet architecture. The effectiveness of the proposed method was verified using experiments on two datasets. The results indicate that, compared to the most accurate change detection results derived using the comparative methods, the detection results determined using the proposed method showed increases of 0.030 and 0.017 in the F1 score and increases of 0.032 and 0.018 in the Kappa coefficient on the two datasets. Therefore, the proposed method can effectively enhance the reliability of change detection results.