SAR image change detection based on discrete wavelet transform and attention-enhanced multi-scale residual network

Synthetic Aperture Radar (SAR) image change detection faces the dual challenge of speckle noise interference and complex structural changes. Most of the traditional methods are based on a single difference image (DI) or shallow network, which leads to difficulties in effectively suppressing speckle noise and extracting features. In this paper, we propose an end-to-end framework that fuses multi-operator difference images, wavelet decomposition reconstruction, and Squeeze-Excitation and Pyramid Pooling Residual Network (SEPP-ResNet). First, we apply a weighted fusion strategy to generate a weighted fusion difference image (WFDI). Secondly, we use the discrete wavelet transform to suppress the speckle noise in the WFDI while enhancing the edge texture information. Finally, we improve the Residual Network (ResNet) by 1) introducing the Squeeze-and-Excitation (SE) attention mechanism to dynamically adjust the channel features and enhance the discriminative features; 2) applying the Pyramid Pooling Module (PPM) for multi-scale contextual feature extraction, which captures the global information while preserving the local detail information. Extensive experiments on four real SAR datasets (Bern, Ottawa, Sulzberger, and Mexico) show that our method achieves outstanding performance. It attains Percentage of Correct Classification (PCC) values of 99.70%, 98.72%, 98.81%, and 98.58%, and Kappa Coefficients (KC) of 87.81%, 95.22%, 96.16%, and 92.07% on the respective datasets, outperforming several state-of-the-art methods.