  In remote sensing images, the existence of the thin cloud is an inevitable
and ubiquitous phenomenon that crucially reduces the quality of imageries and
limits the scenarios of application. Therefore, thin cloud removal is an
indispensable procedure to enhance the utilization of remote sensing images.
Generally, even though contaminated by thin clouds, the pixels still retain
more or less surface information. Hence, different from thick cloud removal,
thin cloud removal algorithms normally concentrate on inhibiting the cloud
influence rather than substituting the cloud-contaminated pixels. Meanwhile,
considering the surface features obscured by the cloud are usually similar to
adjacent areas, the dependency between each pixel of the input is useful to
reconstruct contaminated areas. In this paper, to make full use of the
dependencies between pixels of the image, we propose a Multi-Head Linear
Attention Generative Adversarial Network (MLAGAN) for Thin Cloud Removal. The
MLA-GAN is based on the encoding-decoding framework consisting of multiple
attention-based layers and deconvolutional layers. Compared with six deep
learning-based thin cloud removal benchmarks, the experimental results on the
RICE1 and RICE2 datasets demonstrate that the proposed framework MLA-GAN has
dominant advantages in thin cloud removal.
