Convolutional Neural Processes for Inpainting Satellite Images
Convolutional Neural Processes for Inpainting Satellite Images
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DOI：
10.48550/arxiv.2205.12407
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Alexander Pondaven;M. Bakler;D. Guo;Hamzah Hashim;Martin Ignatov;Harrison Zhu
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作者：
Alexander Pondaven;M. Bakler;D. Guo;Hamzah Hashim;Martin Ignatov;Harrison Zhu
卫星图像的广泛可用性使研究人员能够对疾病动态等复杂系统进行建模。然而，由于测量缺陷，许多卫星图像有缺失值，这使得它们在没有数据填补的情况下无法使用。例如，LANDSAT 7号卫星的扫描线校正器在2003年发生故障，造成约20%的数据丢失。修复涉及根据已知像素预测丢失的内容，这是图像处理中的一个老问题，通常基于偏微分方程或插值方法，但最近的深度学习方法已经显示出了希望。然而，其中许多方法并没有明确考虑到卫星图像固有的时空结构。在这项工作中，我们将卫星图像修复作为一个自然的元学习问题，并建议使用卷积神经过程（ConvNP），将每个卫星图像作为自己的任务或2D回归问题。我们证明了ConvNP在LANDSAT 7卫星图像的扫描线修复问题上的表现优于经典方法和最先进的深度学习修复模型，并对各种分布图像进行了评估。
The widespread availability of satellite images has allowed researchers to model complex systems such as disease dynamics. However, many satellite images have missing values due to measurement defects, which render them unusable without data imputation. For example, the scanline corrector for the LANDSAT 7 satellite broke down in 2003, resulting in a loss of around 20\% of its data. Inpainting involves predicting what is missing based on the known pixels and is an old problem in image processing, classically based on PDEs or interpolation methods, but recent deep learning approaches have shown promise. However, many of these methods do not explicitly take into account the inherent spatiotemporal structure of satellite images. In this work, we cast satellite image inpainting as a natural meta-learning problem, and propose using convolutional neural processes (ConvNPs) where we frame each satellite image as its own task or 2D regression problem. We show ConvNPs can outperform classical methods and state-of-the-art deep learning inpainting models on a scanline inpainting problem for LANDSAT 7 satellite images, assessed on a variety of in and out-of-distribution images.