Change detection method based on style transfer for heterogeneous image feature alignment

Heterogeneous images often contain significant feature discrepancies caused by sensor differences, imaging conditions, and geometric distortions, leading to feature-space inconsistencies that degrade change detection performance. To address this issue, this paper proposes a style transfer–based change detection framework for feature alignment. A style-aware global–local network is developed to reduce information loss and structural distortion during style transfer. By integrating multi-scale linear attention, spatially adaptive feature modulation, and style–content feature attention, it enhances feature representation, preserves structural structure, and improves feature-space consistency. The style-transferred images are used to extract high-quality difference images, reducing heterogeneous feature discrepancies. A histogram-gradient thresholding strategy is applied for reliable sample selection, followed by a convolutional wavelet neural network for accurate change classification. Experimental results demonstrate the effectiveness and superiority of the proposed method in heterogeneous image change detection.