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In remote sensing images, the existence of the thin cloud is an inevitableand ubiquitous phenomenon that crucially reduces the quality of imageries andlimits the scenarios of application. Therefore, thin cloud removal is anindispensable procedure to enhance the utilization of remote sensing images.Generally, even though contaminated by thin clouds, the pixels still retainmore or less surface information. Hence, different from thick cloud removal,thin cloud removal algorithms normally concentrate on inhibiting the cloudinfluence rather than substituting the cloud-contaminated pixels. Meanwhile,considering the surface features obscured by the cloud are usually similar toadjacent areas, the dependency between each pixel of the input is useful toreconstruct contaminated areas. In this paper, to make full use of thedependencies between pixels of the image, we propose a Multi-Head LinearAttention Generative Adversarial Network (MLAGAN) for Thin Cloud Removal. TheMLA-GAN is based on the encoding-decoding framework consisting of multipleattention-based layers and deconvolutional layers. Compared with six deeplearning-based thin cloud removal benchmarks, the experimental results on theRICE1 and RICE2 datasets demonstrate that the proposed framework MLA-GAN hasdominant advantages in thin cloud removal.

Multi-Head Linear Attention Generative Adversarial Network for Thin Cloud Removal