用生成模型修复遥感图像云遮挡，效果优于传统方法。
Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal
- 基于MAE重建模型与GAN框架，采用局部块判别器提升修复精度。
- 在公开数据集上显著优于其他GAN方法，云区还原更自然。
- 适合遥感图像处理、气象监测等需要高清影像的场景。
云遮挡去除在提升遥感图像分析中至关重要，但准确重建被云覆盖区域仍是重大挑战。近年来生成模型的发展使真实图像生成变得可行，为该任务提供了新机遇。鉴于图像生成与云去除任务在概念上的契合性，生成模型展现出巨大潜力。本文提出一种基于生成对抗网络（GAN）框架的深度迁移学习方法，探索新型掩码自编码器（MAE）在云去除中的应用。由于遥感图像复杂度高，我们进一步引入局部块判别器，判断图像每个块是否真实。所提重建式迁移学习方法在云去除性能上显著优于其他GAN方法。尽管部分先进方法因训练/测试数据划分不明确，难以直接对比，但在现有基准上仍取得具有竞争力的结果。
原文摘要 · Abstract (English)
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.
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