<title>Abstract</title> The rapid advancement of deep learning has led to significant achievements in remote sensing object detection. However, domain shift often causes notable performance drops when models trained on one domain are applied to real-world scenarios. Unsupervised domain adaptation (UDA) offers a solution by narrowing domain gaps. Generative adversarial networks (GANs) are commonly used for this purpose, but they can degrade key textures and details in source images. To address this, we propose a me…