  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.
