Automated deformation detection and interpretation using InSAR data and a multi-task ViT model-Reference-Cited by-同舟云学术

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Automated deformation detection and interpretation using InSAR data and a multi-task ViT model

Published:2024-04 Issue: Volume:128 Page:103758
ISSN:1569-8432
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Abdallah Mahmoud^ORCID,Younis Samaa,Wu Songbo^ORCID,Ding Xiaoli

Publisher

Elsevier BV

Reference45 articles.

1. Application of machine learning to classification of volcanic deformation in routinely generated InSAR data;Anantrasirichai;J. Geophys. Res. Solid Earth,2018

2. A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets;Anantrasirichai;Remote Sens. Environ.,2019

3. The application of convolutional neural networks to detect slow, sustained deformation in InSAR time series;Anantrasirichai;Geophys. Res. Lett.,2019

4. Sequential estimator: toward efficient InSAR time series analysis;Ansari;IEEE Trans. Geosci. Remote Sens.,2017

5. Self-supervised contrastive learning for volcanic unrest detection;Bountos;IEEE Geosci. Remote Sens. Lett.,2022

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