Clouds in optical satellite images are a major concern since their presence hinders the ability to carry accurate analysis as well as processing. Presence of clouds also affects the image tasking schedule and results in wastage of valuable storage space on ground as well as space-based systems. Due to these reasons, deriving accurate cloud masks from optical remote-sensing images is an important task. Traditional methods such as threshold-based, spatial filtering for cloud detection in satellite images suffer from lack of accuracy. In recent years, deep learning algorithms have emerged as a promising approach to solve image segmentation problems as it allows pixel-level classification and semantic-level segmentation. In this paper, we introduce a deep-learning model based on hybrid transformer architecture for effective cloud mask generation named CLiSA - Cloud segmentation via Lipschitz Stable Attention network. In this context, we propose an concept of orthogonal self-attention combined with hierarchical cross attention model, and we validate its Lipschitz stability theoretically and empirically. We design the whole setup under adversarial setting in presence of Lov\'asz-Softmax loss. We demonstrate both qualitative and quantitative outcomes for multiple satellite image datasets including Landsat-8, Sentinel-2, and Cartosat-2s. Performing comparative study we show that our model performs preferably against other state-of-the-art methods and also provides better generalization in precise cloud extraction from satellite multi-spectral (MX) images. We also showcase different ablation studies to endorse our choices corresponding to different architectural elements and objective functions.

翻译：光学卫星图像中的云层是主要关注点，因其存在会阻碍精确分析与处理能力。云层还会影响图像任务调度，并导致地面及空间系统的宝贵存储空间浪费。基于这些原因，从光学遥感图像中获取精确云掩膜是一项重要任务。传统方法（如基于阈值的空间滤波）用于卫星图像云检测时存在精度不足的问题。近年来，深度学习算法因支持像素级分类与语义级分割，已成为解决图像分割问题的有效途径。本文提出一种基于混合Transformer架构的深度学习模型CLiSA（Cloud segmentation via Lipschitz Stable Attention network），用于高效生成云掩膜。在此框架下，我们提出正交自注意力与层次交叉注意力相结合的概念，并从理论与实证角度验证其Lipschitz稳定性。我们在对抗性设置下结合Lovász-Softmax损失函数设计整体架构。针对Landsat-8、Sentinel-2和Cartosat-2s等多卫星图像数据集，我们展示了定性与定量结果。通过对比研究，该模型相较于其他先进方法具有更优性能，且在卫星多光谱（MX）图像的精确云层提取中展现出更强的泛化能力。此外，我们通过消融实验验证了不同架构组件与目标函数选择的有效性。