基于遥感影像的变化检测在城市规划、环境监测和灾害评估等领域具有广泛应用。然而卫星遥感影像通常呈现复杂的场景特征和不规则的纹理形状，导致在利用深度学习方法进行变化检测时存在伪变化频繁和精细目标易漏检等问题。针对这些问题，提出了一种基于改进深度卷积生成对抗网络的遥感影像变化检测方法。该方法设计了一种结合坐标注意力的U-Net生成器，以提高模型对复杂场景下精细目标检测的敏感性和准确度，同时采用改进轻型全卷积网络作为判别器，通过生成对抗的方式有效解决了检测特征不完整或缺失的问题。此外，还设计了一种加权损失函数以实现模型训练稳定性与检测精度之间的平衡。通过在公开的变化检测数据集SYSU-CD（Sun Yat-Sen University-change detection）和CDD（change detection dataset）上的实验证明，该方法在F₁得分上相比于基准UNet分别提升了3.02%和5.76%，最终分别达到80.59%和96.25%，均优于其他对比方法。

Remote sensing image change detection is widely applied in urban planning, environmental monitoring, and disaster assessment. However, satellite remote sensing images typically exhibit complex scene characteristics and irregular texture patterns, leading to frequent false changes and missed detection of fine-grained targets when deep learning methods are used for change detection. To address these issues, a remote sensing image change detection method based on an improved deep convolutional generative adversarial network was proposed. A U-Net generator combined with coordinate attention was designed to enhance the model's sensitivity and accuracy in detecting fine-grained targets under complex scenes. Simultaneously, an improved lightweight fully convolutional network was adopted as the discriminator, effectively resolving problems of incomplete or missing detection features through generative adversarial training. Furthermore, a weighted loss function was developed to balance model training stability and detection accuracy. Experiments were conducted on the public change detection datasets SYSU-CD (Sun Yat-Sen University-change detection) and CDD (change detection dataset) demonstrated that the proposed method achieved significant performance improvements. The method achieves improvements of 3.02% and 5.76% in F₁ scores compared to the baseline UNet, reaching final scores of 80.59% and 96.25%, respectively, which are superior to other comparative methods.