Front. Remote Sens.
Sec. Remote Sensing Time Series Analysis
doi: 10.3389/frsen.2022.932491

Land cover Classification of the Alps from InSAR Temporal Coherence Matrices

  • 1UMR5275 Institut des Sciences de la Terre (ISTERRE), France
  • 2Institut de Recherche Pour le Développement (IRD), France
  • 3Université Savoie Mont Blanc, France
Provisionally accepted:
The final, formatted version of the article will be published soon.

Land cover mapping is of great interest in the Alps region for monitoring the surface occupation
changes (e.g. forestation, urbanization, etc). In this pilot study, we investigate how time series
of radar satellite imaging (C-band single-polarized SENTINEL-1 Synthetic Aperture Radar,
SAR), also acquired through clouds, could be an alternative to optical imaging for land cover
segmentation. Concretely, we compute for every location (using SAR pixels over 45 × 45m)
the temporal coherence matrix of the Interferometric SAR (InSAR) phase over one year. This
normalized matrix of size 60 × 60 (60 acquisition dates over one year) summarizes the reflectivity
changes of the land. Two machine learning models, a Support Vector Machine (SVM) and a
Convolutional Neural Network (CNN) have been developed to estimate land cover classification
performances of 6 main land cover classes (such as forests, urban areas, water bodies, or
pastures). The training database was created by projecting to the radar geometry
the reference labeled CORINE Land Cover
(CLC) map on the mountainous area of Grenoble, France. Upon evaluation, both models
demonstrated good performances with an overall accuracy of 78% (SVM) and of 81% (CNN) over
Chambéry area (France). We show how, even with a spatially coarse training database, our model is able
to generalize well, as a large part of the misclassifications are due to a low precision of the ground
truth map. Although some less computationally expensive approaches (using optical data) could
be available, this land cover mapping based on very different information , i.e. patterns of land
changes over a year, could be complementary and thus beneficial; especially in mountainous
regions where optical imaging is not always available due to clouds. Moreover, we demonstrated
that the InSAR temporal coherence matrix is very informative, which could lead in the future to
other applications such as automatic detection of abrupt changes as snow fall or landslides.

Keywords: InSAR (Interferometric Synthetic Aperture Radar), land cover mapping, Temporal coherence (TC), convolutional neural network, Alps monitoring, machine learning

Received: 29 Apr 2022; Accepted: 02 Aug 2022.

Copyright: © 2022 Giffard-Roisin, Boudaour, Doin, Yan and Atto. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Sophie Giffard-Roisin, UMR5275 Institut des Sciences de la Terre (ISTERRE), Gières, France