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Recurrent neural networks for atmospheric noise removal from InSAR time series with missing values
ISPRS Journal of Photogrammetry and Remote Sensing ( IF 10.6 ) Pub Date : 2021-08-27 , DOI: 10.1016/j.isprsjprs.2021.08.009
Zhuoyi Zhao 1 , Zherong Wu 1 , Yi Zheng 1 , Peifeng Ma 1, 2
Affiliation  

Atmospheric noise is one of the primary challenges for improving the accuracy of deformation estimation by InSAR technologies. Temporal filtering methods, like Gaussian filtering, are commonly used to remove the atmospheric noise from the InSAR time series. Such low-pass filters can effectively suppress stochastic noise. Yet, its performance heavily depends on the parameter settings and can easily be affected by the seasonal variations and missing values presented in the InSAR time series. Recurrent neural networks (RNNs) have been successfully adapted in many time series or sequential data applications. Still, there is little work on exploiting the ability of RNNs for modeling InSAR time series. This paper proposes a bidirectional RNN with gated recurrent units (GRU) for removing the atmospheric noise from the InSAR time series. A physical-based method of synthesizing InSAR time series is developed to tackle the lack of training data problem. The proposed GRU model integrates a GRU-D layer for handling the missing values, and all the model components are jointly trained to produce the denoised time series. Besides, we introduce the seasonal factor (SF) signal as an auxiliary input to help the model better capture the seasonality of the deformation and improve the denoising results. Experiments on synthetic datasets and HKIA real-world datasets demonstrate that our proposed GRU model achieves better denoising performance than Gaussian filtering and other RNN baseline models.



中文翻译:

用于从具有缺失值的 InSAR 时间序列中去除大气噪声的循环神经网络

大气噪声是提高 InSAR 技术变形估计精度的主要挑战之一。时间滤波方法,如高斯滤波,通常用于从 InSAR 时间序列中去除大气噪声。这种低通滤波器可以有效抑制随机噪声。然而,它的性能在很大程度上取决于参数设置,并且很容易受到 InSAR 时间序列中呈现的季节性变化和缺失值的影响。循环神经网络 (RNN) 已成功应用于许多时间序列或序列数据应用程序。尽管如此,利用 RNN 的能力对 InSAR 时间序列进行建模的工作很少。本文提出了一种带有门控循环单元 (GRU) 的双向 RNN,用于从 InSAR 时间序列中去除大气噪声。开发了一种基于物理的合成 InSAR 时间序列的方法来解决缺乏训练数据的问题。所提出的 GRU 模型集成了一个用于处理缺失值的 GRU-D 层,并且所有模型组件都经过联合训练以产生去噪时间序列。此外,我们引入季节性因子(SF)信号作为辅助输入,以帮助模型更好地捕捉变形的季节性并改善去噪结果。在合成数据集和 HKIA 真实数据集上的实验表明,我们提出的 GRU 模型比高斯滤波和其他 RNN 基线模型实现了更好的去噪性能。并且所有模型组件都经过联合训练以生成去噪时间序列。此外,我们引入季节性因子(SF)信号作为辅助输入,以帮助模型更好地捕捉变形的季节性并改善去噪结果。在合成数据集和 HKIA 真实数据集上的实验表明,我们提出的 GRU 模型比高斯滤波和其他 RNN 基线模型实现了更好的去噪性能。并且所有模型组件都经过联合训练以生成去噪时间序列。此外,我们引入季节性因子(SF)信号作为辅助输入,以帮助模型更好地捕捉变形的季节性并改善去噪结果。在合成数据集和 HKIA 真实数据集上的实验表明,我们提出的 GRU 模型比高斯滤波和其他 RNN 基线模型实现了更好的去噪性能。

更新日期:2021-08-29
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