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.

翻译：云分析是天气和气候科学的关键组成部分，对灾害管理等众多领域具有重要影响。然而，在遥感领域实现如云图分割等精细化云分析仍面临挑战，其根本原因在于难以获取准确标注，导致训练数据中存在显著标注误差。现有方法通常假设可靠的分割标注可用，这限制了它们的整体性能。为解决这一固有局限，我们提出了一种创新的模型无关的云自适应标注（CAL）方法，该方法通过迭代操作提升训练数据标注质量，进而改善所学模型的性能。我们的方法首先利用原始标注训练一个云图分割模型。随后，引入一个可训练的像素强度阈值，用于在训练过程中自适应地标注云图训练图像。生成的新标注随后被用于对模型进行微调。在多个标准云图分割基准上进行的广泛实验表明，我们的方法能显著提升现有分割模型的性能。与现有多种替代方案相比，我们的CAL方法取得了最新的最佳结果。