Learning to detect cloud and snow in remote sensing images from noisy
  labels
Zili Liu Hao Chen Wenyuan Li Keyan Chen Zipeng Qi Chenyang Liu Zhengxia Zou Zhenwei Shi
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.