Prediction of InSAR time-series deformation using deep convolutional neural networks-Reference-Cited by-同舟云学术

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Prediction of InSAR time-series deformation using deep convolutional neural networks

Published:2019-11-18 Issue:2 Volume:11 Page:137-145
ISSN:2150-704X
Container-title:Remote Sensing Letters
language:en
Short-container-title:Remote Sensing Letters

Author:

Ma Peifeng¹²^ORCID,Zhang Fan³^ORCID,Lin Hui⁴

Affiliation:

1. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China

2. Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China

3. Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China

4. School of Geography and Environment, Jiangxi Normal University, Nanchang, China

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Earth and Planetary Sciences (miscellaneous)

Link

https://www.tandfonline.com/doi/pdf/10.1080/2150704X.2019.1692390

Reference9 articles.

1. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms

2. Persistent Scatterer Interferometry: A review

3. Land subsidence prediction in Beijing based on PS-InSAR technique and improved Grey-Markov model

4. A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR

5. Permanent scatterers in SAR interferometry

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2. Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm–Convolutional Neural Network–Long Short-Term Memory Model;Sensors;2024-04-20

3. Automated deformation detection and interpretation using InSAR data and a multi-task ViT model;International Journal of Applied Earth Observation and Geoinformation;2024-04

4. Prediction of airport runway settlement using an integrated SBAS-InSAR and BP-EnKF approach;Information Sciences;2024-04

5. Forecasting surface movements based on PSI time series using machine learning algorithms;International Journal of Remote Sensing;2024-03-27

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