Abstract:
Many earth observation activities rely on optical remote
sensing data. The optical remote sensing imagery is exploited in various applications like farmland monitoring,
land use, land cover, 3D city models, vegetation growth,
and disaster mitigation. Despite all, cloud cover significantly impacts on spatial and temporal accessibility of the
earth observation. Since the first observation, it has been
one persistent difficulty for optical remote sensing. For
decades, researchers have been studying to remove clouds
from optical images. The procedure of clearing the clouds
becomes more difficult as they thicken. In such instances, it
is customary to reconstruct utilizing additional images such
as synthetic aperture radar (SAR) or near-infrared (NIR).
In this paper, we propose a two-stage architecture-based
cloud removal framework. The first stage of our network
translates SAR and optical cloudy images to synthetic optical (RGB) image using the conditional Generative Adversarial Network (cGAN) and the second stage reconstructs
the cloud-free image by fusing the synthetic optical (RGB)
and cloudy optical image. The network was tested on the
real cloudy images and the proposed method was compared
with the state-of-the-art models and showed better results
for cloud removal.
