Cloud analysis is a critical component of weather and climate science,
impacting various sectors like disaster management. However, achieving
fine-grained cloud analysis, such as cloud segmentation, in remote sensing
remains challenging due to the inherent difficulties in obtaining accurate
labels, leading to significant labeling errors in training data. Existing
methods often assume the availability of reliable segmentation annotations,
limiting their overall performance. To address this inherent limitation, we
introduce an innovative model-agnostic Cloud Adaptive-Labeling (CAL) approach,
which operates iteratively to enhance the quality of training data annotations
and consequently improve the performance of the learned model. Our methodology
commences by training a cloud segmentation model using the original
annotations. Subsequently, it introduces a trainable pixel intensity threshold
for adaptively labeling the cloud training images on the fly. The newly
generated labels are then employed to fine-tune the model. Extensive
experiments conducted on multiple standard cloud segmentation benchmarks
demonstrate the effectiveness of our approach in significantly boosting the
performance of existing segmentation models. Our CAL method establishes new
state-of-the-art results when compared to a wide array of existing
alternatives.

通过创新的模型无关的云自适应标注（CAL）方法来提高云分割模型的性能，该方法使用原始标注进行训练，然后利用可调节的像素强度阈值对云训练图像进行自适应标注，最终通过此方法显著提高现有分割模型的性能。