Optical and SAR image change detection based on Siamses attention enhanced method

Accurate change detection is critical for applications like disaster assessment, building damage analysis, and information retrieval. However, existing methods predominantly rely on homologous remote sensing data (e.g., optical/SAR image pairs), limiting their ability to achieve timely and precise change extraction. To address this limitation, this paper introduces the SAE_UNet++, a change detection network specifically designed for heterologous remote sensing images. The architecture integrates: (1) heterologous image translation module and (2) a Siamese attention enhanced detection module. The heterologous image translation module utilizes a conditional GAN to align optical and SAR images in a unified feature space, minimizing information loss during translation. The Siamese attention enhanced change detection module processes dual-temporal images through weight-shared encoders for local feature extraction. Multi-scale convolutional block attention mechanisms (CBAM) augment the Siamese architecture to model global context information and two branch feature fusion. The decoder subsequently generates multi-level predictions for optimization. To evaluate SAE_UNet++'s efficacy in heterogeneous change detection, we evaluated it on two flood event datasets. The model achieved F1 scores of 95.91% (Gloucester I) and 77.99% (California), with visualization results showing closer alignment to ground truth than comparative methods, demonstrating state-of-the-art performance.