HLSTM: Heterogeneous Long Short-Term Memory Network for Large-Scale InSAR Ground Subsidence Prediction

Accurate prediction of ground subsidence is of great significance for the prevention and mitigation of this type of geological disaster. It is still a challenge when wide area is concerned. In this article, a heterogeneous long short-term memory (HLSTM) network is proposed for large-scale ground sub...

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Bibliographic Details

Published in:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Main Authors:	Qinghao Liu, Yonghong Zhang, Jujie Wei, Hongan Wu, Min Deng
Format:	Article
Language:	English
Published:	IEEE 2021-01-01
Subjects:	Deep learning heterogeneity interferometric synthetic aperture radar (InSAR) long short-term memory (LSTM) subsidence prediction
Online Access:	https://ieeexplore.ieee.org/document/9521743/