论文标题
通过空间注意力产生的对抗网络删除远程感测图像的云
Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network
论文作者
论文摘要
由于其高分辨率和稳定的几何特性，光学遥感图像已在许多字段中广泛使用。但是，遥感图像不可避免地会受到气候，尤其是云的影响。在分析之前，在高分辨率遥感卫星图像中删除云是必不可少的预处理步骤。为了大规模训练数据，神经网络在许多图像处理任务中都取得了成功，但是使用神经网络在遥感图像中删除云仍然相对较小。我们采用生成性的对抗网络来解决此任务，并将空间注意机制引入遥感图像云删除任务中，提出了一个名为“空间注意力生成的对抗网络（SPA GAN）”的模型，模仿人类的视觉机制，并识别并以云到云的空间质量恢复云领域，从而使云领域变得更好，并将其重点融合到这些区域中，并恢复了这些图像，并恢复了这些图像的概述，概述了概述的概述，概念云是概述的概述，并将其构成云层的概述，并将其构想为云量的概述，并将其构想为云的概述，并将其构想为云的概述，并将其识别为云的概述，并将其识别为云的概述，并将其识别为云的概述，并将其集中在云方面。
Optical remote sensing imagery has been widely used in many fields due to its high resolution and stable geometric properties. However, remote sensing imagery is inevitably affected by climate, especially clouds. Removing the cloud in the high-resolution remote sensing satellite image is an indispensable pre-processing step before analyzing it. For the sake of large-scale training data, neural networks have been successful in many image processing tasks, but the use of neural networks to remove cloud in remote sensing imagery is still relatively small. We adopt generative adversarial network to solve this task and introduce the spatial attention mechanism into the remote sensing imagery cloud removal task, proposes a model named spatial attention generative adversarial network (SpA GAN), which imitates the human visual mechanism, and recognizes and focuses the cloud area with local-to-global spatial attention, thereby enhancing the information recovery of these areas and generating cloudless images with better quality...
