Attention-Guided Generative Adversarial Network for False Alarm-Resistant Change Detection in Remote Sensing Orthophotos

Orthophoto change detection is used to find real land cover changes in urban monitoring, disaster assessment, and environmental management. In practice, however, multi-temporal orthophotos are rarely identical in geometry and radiometry even after standard preprocessing. Small residual misregistration, local building displacement, shadow movement, and illumination differences can produce edge-like responses that look like change but do not correspond to any land cover transition. These false alarms increase manual checking costs and reduce the reliability of change maps. This study addresses that practical problem by proposing an attention-guided conditional adversarial framework, named Attention-GAN, for false alarm-resistant orthophoto change detection. The aim is not to detect small perturbations as changes but to detect real land cover changes while suppressing responses to nuisance variations that should be treated as unchanged. The framework integrates a multi-scale spatial attention module, a channel attention module, and a PatchGAN discriminator. It also introduces perturbation-negative training pairs, where controlled geometric and radiometric perturbations are applied to unchanged image pairs and assigned all-zero change masks. Experiments on LEVIR-CD, WHU-CD, and DSIFN-CD show competitive or moderately higher accuracy than the selected representative baselines, with F1 scores of 91.2%, 92.45%, and 93.18%, respectively. In the ablation experiment, the false change rate on perturbation-negative validation pairs is reduced to 4.9%. Repeated-run statistics and ablation results indicate that the proposed training strategy mainly improves robustness by reducing false alarms under the evaluated perturbation range. The results support the use of controlled nuisance perturbations as a reproducible way to train and evaluate false alarm resistance, while broader validation under real multi-view, seasonal, and cross-sensor distortions remains necessary.