Dynamic Bilinear Fusion Network for Synthetic Aperture Radar Image Change Detection

Change detection from synthetic aperture radar (SAR) imagery is critical in remote sensing research. Existing methods have made significant progress in the application of convolutional neural networks (CNNs) and attention mechanisms. However, traditional CNNs suffer the limitations in feature representation due to their depth and width constraints, and struggle to effectively capture complex interactions between image features. To address these issues, we propose a novel dynamic bilinear fusion network (DBFNet) for change detection in SAR imagery. First, to compensate for the lack of traditional convolutional representation capability, we design a dynamic shift convolution module that adaptively aggregates multiple convolution kernels and shifts pixels, enabling richer and more detailed features to be extracted. Second, a bilinear fusion module (BFM) is designed to generate the bilinear joint representation between parallel features by computing a matrix outer product of feature maps. The parallel features include both intraimage and interimage features, thereby effectively modeling the complex interactions and capturing the dependence relationship between spatiotemporal features. The experimental results on three real SAR datasets demonstrate the superior performance of DBFNet compared to existing state-of-the-art methods.