论文标题
使用部分卷积的深神经网络对卫星图像时间序列的有效数据驱动的差距填充
Efficient data-driven gap filling of satellite image time series using deep neural networks with partial convolutions
论文作者
论文摘要
卫星图像时间序列中的大量差距通常会使深度学习模型（例如卷积神经网络用于时空建模）的应用变得复杂。基于计算机视觉介绍的先前工作，本文显示了如何将三维时空部分卷积用作神经网络中的层，以填补卫星图像时间序列中的空白。为了评估该方法，我们将类似U-NET的模型应用于Sentinel-5p卫星的准全球碳一氧化碳观测值不完整的图像时间序列。预测错误与两种考虑的统计方法相当，而预测的计算时间最多要快三个数量级，这使得该方法适用于处理大量卫星数据。可以将部分卷积添加到其他类型的神经网络中，从而使与现有深度学习模型集成相对容易。但是，该方法没有量化预测错误，需要进一步的研究来理解和提高模型可传递性。时空部分卷积的实现和U-NET型模型可作为开源软件可用。
The abundance of gaps in satellite image time series often complicates the application of deep learning models such as convolutional neural networks for spatiotemporal modeling. Based on previous work in computer vision on image inpainting, this paper shows how three-dimensional spatiotemporal partial convolutions can be used as layers in neural networks to fill gaps in satellite image time series. To evaluate the approach, we apply a U-Net-like model on incomplete image time series of quasi-global carbon monoxide observations from the Sentinel-5P satellite. Prediction errors were comparable to two considered statistical approaches while computation times for predictions were up to three orders of magnitude faster, making the approach applicable to process large amounts of satellite data. Partial convolutions can be added as layers to other types of neural networks, making it relatively easy to integrate with existing deep learning models. However, the approach does not quantify prediction errors and further research is needed to understand and improve model transferability. The implementation of spatiotemporal partial convolutions and the U-Net-like model is available as open-source software.
