论文标题
处理遥感中的图像和标签分辨率不匹配
Handling Image and Label Resolution Mismatch in Remote Sensing
论文作者
论文摘要
尽管语义细分已经在视觉文献中进行了大量探索，但在遥感领域仍然存在独特的挑战。一个挑战是如何处理额外图像和地面标签源之间的分辨率不匹配，这是由于地面样品距离的差异。为了说明这个问题，我们介绍了一个新的数据集，并使用它来展示现有策略中固有的弱点，这些弱点天真地删除目标标签以匹配图像分辨率。取而代之的是，我们提出了一种使用低分辨率标签（无需升级）监督的方法，但利用了一组高分辨率标签的示例集来指导学习过程。我们的方法结合了区域聚集，对抗性学习和自我监督的预处理，以产生细粒度的预测，而无需高分辨率注释。广泛的实验证明了我们方法的现实适用性。
Though semantic segmentation has been heavily explored in vision literature, unique challenges remain in the remote sensing domain. One such challenge is how to handle resolution mismatch between overhead imagery and ground-truth label sources, due to differences in ground sample distance. To illustrate this problem, we introduce a new dataset and use it to showcase weaknesses inherent in existing strategies that naively upsample the target label to match the image resolution. Instead, we present a method that is supervised using low-resolution labels (without upsampling), but takes advantage of an exemplar set of high-resolution labels to guide the learning process. Our method incorporates region aggregation, adversarial learning, and self-supervised pretraining to generate fine-grained predictions, without requiring high-resolution annotations. Extensive experiments demonstrate the real-world applicability of our approach.
