标题： 基于重建模型的Patch-GAN迁移学习用于云去除 显示英文标题

标题： Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal

摘要： 云去除在增强遥感图像分析中起着至关重要的作用，但准确重建被云遮挡的区域仍然是一个重大挑战。 生成模型的最新进展使得生成逼真的图像变得更加容易，为这项任务提供了新的机会。 鉴于图像生成和云去除任务之间的概念一致性，生成模型为解决遥感中的云去除问题提供了一个有前景的方法。 在本工作中，我们提出了一种基于生成对抗网络（GAN）框架的深度迁移学习方法，以探索新型掩码自编码器（MAE）图像重建模型在云去除中的潜力。 由于遥感图像的复杂性，我们进一步提出使用逐块判别器来判断图像的每个块是否真实。 所提出的重建迁移学习方法在云去除性能方面相比其他基于GAN的方法表现出显著的改进。 此外，尽管由于其训练/测试数据划分的细节不明确，与一些最先进的云去除技术进行直接比较受到限制，但所提出的模型在现有基准上取得了具有竞争力的结果。 显示英文摘要

摘要： Cloud removal plays a crucial role in enhancing remote sensing image analysis, yet accurately reconstructing cloud-obscured regions remains a significant challenge. Recent advancements in generative models have made the generation of realistic images increasingly accessible, offering new opportunities for this task. Given the conceptual alignment between image generation and cloud removal tasks, generative models present a promising approach for addressing cloud removal in remote sensing. In this work, we propose a deep transfer learning approach built on a generative adversarial network (GAN) framework to explore the potential of the novel masked autoencoder (MAE) image reconstruction model in cloud removal. Due to the complexity of remote sensing imagery, we further propose using a patch-wise discriminator to determine whether each patch of the image is real or not. The proposed reconstructive transfer learning approach demonstrates significant improvements in cloud removal performance compared to other GAN-based methods. Additionally, whilst direct comparisons with some of the state-of-the-art cloud removal techniques are limited due to unclear details regarding their train/test data splits, the proposed model achieves competitive results based on available benchmarks.