Surface deformation prediction based on TS-InSAR technology and long short-term memory networks-Reference-Cited by-同舟云学术

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Surface deformation prediction based on TS-InSAR technology and long short-term memory networks

Published:2022 Issue:7 Volume:26 Page:1326-1341
ISSN:1007-4619
Container-title:National Remote Sensing Bulletin
language:en
Short-container-title:

Author:

CHEN Yi, ,HE Yi,ZHANG Lifeng,CHEN Baoshan,HE Xu,PU Hongyu,CAO Shengpeng,GAO Liya,YANG Wang, ,

Publisher

Aerospace Information Research Institute, Chinese Academy of Sciences

Subject

Earth and Planetary Sciences (miscellaneous),Instrumentation,Geography, Planning and Development

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Urban ground subsidence monitoring and prediction using time-series InSAR and machine learning approaches: a case study of Tianjin, China;Environmental Earth Sciences;2024-08

2. Revealing Large-Scale Surface Subsidence in Jincheng City's Mining Clusters Using MT-InSAR and VMD-SSA-LSTM Time Series Prediction Model;2024-06-07

3. Urban Ground Subsidence Monitoring and Prediction Using Time-Series InSAR and Machine Learning Approaches: A Case Study of Tianjin, China;2024-05-23

4. Spatio-Temporal Characteristics and Driving Mechanism of Alpine Peatland InSAR Surface Deformation—A Case Study of Maduo County, China;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

5. GLER-BiGRUnet: A Surface Deformation Prediction Model Fusing Multiscale Features of InSAR Deformation Information and Environmental Factors;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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