Remote sensing change detection (RSCD) is crucial for ur- ban monitoring, environmental protection, and disaster as- sessment, but small-sample scenarios often lead to overfitting and inaccurate predictions on unseen data. To address this, we propose GSAG-CDGAN, an end-to-end framework integrat- ing Selective Noise Augmentation (SNA) to mitigate overfit- ting, an Attention-Guided Adversarial Network (AGAN) to enhance structural consistency, and a Perceptual Loss Mod- ule (PLM) to preserve semantic consistency. Experiments on CDData-50 show that GSAG-CDGAN improves F1-Score from 0.6954 to 0.8851, with notable gains in Recall and IoU, demonstrating enhanced robustness under small-sample con- ditions. Further evaluation on the WHU-CD dataset yields an F1-Score of 0.9502, confirming strong cross-dataset general- ization and the method’s effectiveness in diverse scenarios.