Interferometric SAR (InSAR) algorithms exploit synthetic aperture radar (SAR) images to estimate ground displacements, which are updated at each new satellite acquisition, over wide areas. The analysis of the resulting time series finds its application, among others, in monitoring tasks regarding seismic faults, subsidence, landslides, and urban structures, for which an accurate and timely response is required. Typical analyses consist of identifying among the numerous time series the ones that exhibit an anomalous displacement, thus deserving to be further investigated. In practice, this is realised by selecting the time series which are characterised by trend changes w.r.t. the historical behaviour. In this work, we propose a Deep Learning approach for change point detection in InSAR time series. The designed architecture combines Long Short-Term Memory (LSTM) cells, to model the temporal correlation among samples in the input time series, and Time-Gated LSTM (TGLSTM) cells, to consider the sampling rate as additional information during learning. We further propose a solution to the lack of ground truth by developing a suitable pipeline for realistic data simulation. The method has been developed and validated through a large suite of experiments. Both quantitative and qualitative analyses have been conducted to demonstrate the detection capabilities of the learned model and how it is a valid alternative to the statistical reference algorithm. We further applied the developed method in a real continuous monitoring project to analyse InSAR time series over the Tuscany region in Italy, proving its effectiveness in the real domain.

A Deep Learning Approach for Change Points Detection in InSAR Time Series

Lattari F.;Matteucci M.
2022

Abstract

Interferometric SAR (InSAR) algorithms exploit synthetic aperture radar (SAR) images to estimate ground displacements, which are updated at each new satellite acquisition, over wide areas. The analysis of the resulting time series finds its application, among others, in monitoring tasks regarding seismic faults, subsidence, landslides, and urban structures, for which an accurate and timely response is required. Typical analyses consist of identifying among the numerous time series the ones that exhibit an anomalous displacement, thus deserving to be further investigated. In practice, this is realised by selecting the time series which are characterised by trend changes w.r.t. the historical behaviour. In this work, we propose a Deep Learning approach for change point detection in InSAR time series. The designed architecture combines Long Short-Term Memory (LSTM) cells, to model the temporal correlation among samples in the input time series, and Time-Gated LSTM (TGLSTM) cells, to consider the sampling rate as additional information during learning. We further propose a solution to the lack of ground truth by developing a suitable pipeline for realistic data simulation. The method has been developed and validated through a large suite of experiments. Both quantitative and qualitative analyses have been conducted to demonstrate the detection capabilities of the learned model and how it is a valid alternative to the statistical reference algorithm. We further applied the developed method in a real continuous monitoring project to analyse InSAR time series over the Tuscany region in Italy, proving its effectiveness in the real domain.
Data models
Deep Learning
InSAR
LSTM
Synthetic aperture radar
Time series analysis
Training
File in questo prodotto:
File Dimensione Formato  
Final_Manuscript.pdf

accesso aperto

Descrizione: Articolo principale
: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 3.73 MB
Formato Adobe PDF
3.73 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1208883
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact