Detecting clouds and snow in remote sensing images is an essential
preprocessing task for remote sensing imagery. Previous works draw inspiration
from semantic segmentation models in computer vision, with most research
focusing on improving model architectures to enhance detection performance.
However, unlike natural images, the complexity of scenes and the diversity of
cloud types in remote sensing images result in many inaccurate labels in cloud
and snow detection datasets, introducing unnecessary noises into the training
and testing processes. By constructing a new dataset and proposing a novel
training strategy with the curriculum learning paradigm, we guide the model in
reducing overfitting to noisy labels. Additionally, we design a more
appropriate model performance evaluation method, that alleviates the
performance assessment bias caused by noisy labels. By conducting experiments
on models with UNet and Segformer, we have validated the effectiveness of our
proposed method. This paper is the first to consider the impact of label noise
on the detection of clouds and snow in remote sensing images.

通过构建新的数据集、提出新的训练策略以及设计更适用的模型性能评估方法，本文首次考虑标签噪声对遥感图像中云和雪的检测的影响，并验证了所提出方法的有效性。