标题： SU-ESRGAN：用于卫星和无人机影像超分辨率的语义和不确定性感知ESRGAN，适用于跨领域评估的微调 显示英文标题

标题： SU-ESRGAN: Semantic and Uncertainty-Aware ESRGAN for Super-Resolution of Satellite and Drone Imagery with Fine-Tuning for Cross Domain Evaluation

摘要： 生成对抗网络（GANs）已经实现了图像的现实超分辨率（SR），然而，它们在语义一致性以及每个像素的置信度方面存在不足，这限制了它们在关键遥感应用中的可信度，如灾害响应、城市规划和农业。 本文介绍了语义和不确定性感知的ESRGAN（SU-ESRGAN），这是第一个为卫星图像设计的SR框架，结合了ESRGAN、通过DeepLabv3进行分割损失以保留类别细节以及蒙特卡洛丢弃以生成像素级不确定性图。 SU-ESRGAN在航空图像上的结果（PSNR、SSIM、LPIPS）与基准ESRGAN相当。 这种新模型在使用广角（FoV）相机的卫星系统或无人机（UAVs）中具有价值，以空间分辨率为代价换取覆盖范围。 模块化设计允许在无人机数据管道中集成，用于机载或后期处理SR，以增强由于运动模糊、压缩和传感器限制导致的图像。 此外，该模型进行了微调，以评估其在跨域应用中的性能。 测试是在两个基于无人机的数据集上进行的，这两个数据集在高度和成像视角上有所不同。 微调模型的性能评估显示，对航空海事无人机数据集有更强的适应性，其成像特征与训练数据一致，突显了在SR应用中领域感知训练的重要性。 显示英文摘要

摘要： Generative Adversarial Networks (GANs) have achieved realistic super-resolution (SR) of images however, they lack semantic consistency and per-pixel confidence, limiting their credibility in critical remote sensing applications such as disaster response, urban planning and agriculture. This paper introduces Semantic and Uncertainty-Aware ESRGAN (SU-ESRGAN), the first SR framework designed for satellite imagery to integrate the ESRGAN, segmentation loss via DeepLabv3 for class detail preservation and Monte Carlo dropout to produce pixel-wise uncertainty maps. The SU-ESRGAN produces results (PSNR, SSIM, LPIPS) comparable to the Baseline ESRGAN on aerial imagery. This novel model is valuable in satellite systems or UAVs that use wide field-of-view (FoV) cameras, trading off spatial resolution for coverage. The modular design allows integration in UAV data pipelines for on-board or post-processing SR to enhance imagery resulting due to motion blur, compression and sensor limitations. Further, the model is fine-tuned to evaluate its performance on cross domain applications. The tests are conducted on two drone based datasets which differ in altitude and imaging perspective. Performance evaluation of the fine-tuned models show a stronger adaptation to the Aerial Maritime Drone Dataset, whose imaging characteristics align with the training data, highlighting the importance of domain-aware training in SR-applications.