SU-ESRGAN提升遥感图像超分辨率，同时输出语义与置信度信息。
SU-ESRGAN: Semantic and Uncertainty-Aware ESRGAN for Super-Resolution of Satellite and Drone Imagery with Fine-Tuning for Cross Domain Evaluation
- 融合分割损失与蒙特卡洛丢弃，实现像素级语义保留与不确定性估计。
- 在航拍图像上保持与基线模型相当的PSNR、SSIM和LPIPS性能。
- 适合无人机/卫星系统中需高可信度超分的场景，支持跨域适应评估。
生成对抗网络（GAN）虽能实现逼真的图像超分辨率（SR），但缺乏语义一致性与像素级置信度，限制其在灾害响应、城市规划和农业等关键遥感应用中的可信度。本文提出首个专为卫星影像设计的语义与不确定性感知超分辨率框架——SU-ESRGAN，整合ESRGAN、DeepLabv3分割损失以保留类别细节，并采用蒙特卡洛丢弃生成像素级不确定性图。该模型在航拍图像上的表现（PSNR、SSIM、LPIPS）与基线ESRGAN相当。模块化设计使其可集成于无人机数据处理流程，用于机载或事后处理，提升因运动模糊、压缩及传感器限制导致的图像质量。进一步通过微调评估其跨域适应能力，在两个不同高度与成像视角的无人机数据集上测试，结果显示模型对与训练数据特征相近的航拍海事无人机数据集适应性更强，凸显了领域感知训练在超分辨率应用中的重要性。
原文摘要 · Abstract (English)
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.
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