论文标题
多头线性注意生成对抗网络，用于去除薄云
Multi-Head Linear Attention Generative Adversarial Network for Thin Cloud Removal
论文作者
论文摘要
在遥感图像中，薄云的存在是一种不可避免且普遍存在的现象，它至关重要地降低了成像质量并限制了应用程序的情况。因此，薄云的去除是一种必不可少的程序，可以增强遥感图像的利用。通常，即使被薄云污染，像素仍然保留或多或少或多或少地表面信息。因此，与厚云的去除不同，薄云去除算法通常集中于抑制云的影响，而不是取代云污染的像素。同时，考虑到云所掩盖的表面特征通常与相邻区域相似，输入的每个像素之间的依赖性对于重建受污染的区域很有用。在本文中，为了充分利用图像像素之间的依赖关系，我们提出了一个多头线性注意生成的对抗网络（Mlagan），以进行薄云的去除。 MLA-GAN基于编码解码框架，该框架由多个基于注意的层和反向倾斜层组成。与六个基于深度学习的薄云去除基准相比，Rice1和Rice2数据集的实验结果表明，所提出的框架MLA-GAN在去除薄云方面具有主要的优势。
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.
