论文标题
通过多图像融合来检测单图像云
Single Image Cloud Detection via Multi-Image Fusion
论文作者
论文摘要
遥感（例如云，雪和阴影）捕获的图像中的伪影，针对各种任务（包括语义分割和对象检测）面临的挑战。开发用于识别此类工件的算法的主要挑战是收集带注释的培训数据的成本。在这项工作中，我们探讨了如何利用多图像融合的最新进展来引导单像云检测。我们证明，优化估计图像质量的网络也隐含地学习以检测云。为了支持对我们方法的培训和评估，我们收集了大型Sentinel-2图像数据集，以及用于土地覆盖的人均语义标签。通过各种实验，我们证明我们的方法减少了对注释训练数据的需求，并改善了云检测性能。
Artifacts in imagery captured by remote sensing, such as clouds, snow, and shadows, present challenges for various tasks, including semantic segmentation and object detection. A primary challenge in developing algorithms for identifying such artifacts is the cost of collecting annotated training data. In this work, we explore how recent advances in multi-image fusion can be leveraged to bootstrap single image cloud detection. We demonstrate that a network optimized to estimate image quality also implicitly learns to detect clouds. To support the training and evaluation of our approach, we collect a large dataset of Sentinel-2 images along with a per-pixel semantic labelling for land cover. Through various experiments, we demonstrate that our method reduces the need for annotated training data and improves cloud detection performance.
