  In remote sensing images, the presence of thick cloud accompanying cloud
shadow is a high probability event, which can affect the quality of subsequent
processing and limit the scenarios of application. Hence, removing the thick
cloud and cloud shadow as well as recovering the cloud-contaminated pixels is
indispensable to make good use of remote sensing images. In this paper, a novel
thick cloud removal method for remote sensing images based on temporal
smoothness and sparsity-regularized tensor optimization (TSSTO) is proposed.
The basic idea of TSSTO is that the thick cloud and cloud shadow are not only
sparse but also smooth along the horizontal and vertical direction in images
while the clean images are smooth along the temporal direction between images.
Therefore, the sparsity norm is used to boost the sparsity of the cloud and
cloud shadow, and unidirectional total variation (UTV) regularizers are applied
to ensure the unidirectional smoothness. This paper utilizes alternation
direction method of multipliers to solve the presented model and generate the
cloud and cloud shadow element as well as the clean element. The cloud and
cloud shadow element is purified to get the cloud area and cloud shadow area.
Then, the clean area of the original cloud-contaminated images is replaced to
the corresponding area of the clean element. Finally, the reference image is
selected to reconstruct details of the cloud area and cloud shadow area using
the information cloning method. A series of experiments are conducted both on
simulated and real cloud-contaminated images from different sensors and with
different resolutions, and the results demonstrate the potential of the
proposed TSSTO method for removing cloud and cloud shadow from both qualitative
and quantitative viewpoints.
