Prediction of InSAR time-series deformation using deep convolutional neural networks
Prediction of InSAR time-series deformation using deep convolutional neural networks
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DOI：
10.1080/2150704x.2019.1692390
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发表时间：
2020-02-01
影响因子：
2.3
通讯作者：
Lin, Hui
中科院分区：
文献类型：
--
作者：
Ma, Peifeng;Zhang, Fan;Lin, Hui
预测变形对于发布异常情况的早期预警和实施及时的补救措施至关重要。本文提出了一种基于深度卷积神经网络（DCNN）的数据驱动的干涉合成孔径雷达（干涉合成孔径雷达）时间序列形变预测方法。我们在填海土地上兴建的香港国际机场进行试验。结果表明，DCNN能够预测复垦土地的线性沉降和建筑物的非线性热膨胀。与监测变形的毫米级精度相比，平均内部误差（0.3 mm）可以忽略不计，这表明DCNN非常接近监测变形值。经地面数据验证，预测结果的均方根误差为3 mm，与实测结果的精度相当。结果表明，DCNN的有效性，短期预测的干涉合成孔径雷达时间序列的变形，这可能是潜在的用于预警系统。
Predicting deformation is crucial to issue early warnings of abnormal conditions and implement timely remedial actions. Herein, we propose a data-driven method based on deep convolutional neural networks (DCNN) to predict interferometric synthetic aperture radar (InSAR) time-series deformation. We conducted experiments at the Hong Kong International Airport built on reclaimed lands. The results showed that the DCNN was able to predict the linear settlement of the reclaimed lands and nonlinear thermal expansion of the buildings. The mean internal error (0.3 mm) was negligible compared with the millimetre-level accuracy of the monitored deformation, indicating that the DCNN approximates the monitored deformation values very well. The root mean square error of the predicted deformation in the subsequent year was 3 mm after validation using ground data, which was comparable to the accuracy of the monitored deformation. The results demonstrated the effectiveness of the DCNN for short-term prediction of InSAR time-series deformation, which can be potentially used in early warning systems.