Removing Atmospheric Noise from InSAR Interferograms in Mountainous
Regions with a Convolutional Neural Network
Abstract
Atmospheric errors in interferometric synthetic aperture radar
(InSAR)-derived estimates of surface deformation often obscure real
displacement signals, especially in mountainous regions. As climate
change disproportionately impacts the mountain cryosphere, developing a
technique for atmospheric correction that performs well in high-relief
terrain is increasingly important. Here, we developed and implemented a
statistical machine learning-based atmospheric correction that relies on
the differing spatial and topographic characteristics of periglacial
features and atmospheric noise. Our correction is applied at the native
spatial and temporal resolution of the InSAR data (40 m, 12 days), does
not require external atmospheric data, and can correct both stratified
and turbulent atmospheric noise. Using Sentinel-1 data from 2015-2022,
we trained a convolutional neural network (CNN) on atmospheric noise
from 136 short-baseline interferograms and displacement signals from
time-series inversion of 337 interferograms. The CNN correction was then
tested on a densely connected network of 202 Sentinel-1 interferograms
which were inverted to create a displacement time series. We used the
Rocky Mountains in Colorado as our training, validation, and testing
areas. When applied to our validation data, our correction offers a
690% improvement in performance over a global meteorological
reanalysis-based correction and a 209% improvement over a high-pass
filter correction. We found that our correction reveals previously
hidden time-dependent kinematic behavior of three representative rock
glaciers in our testing dataset. Our flexible, robust approach can be
used to correct arbitrary InSAR data to analyze subtle surface
deformation signals for a range of science and engineering applications.