Snow cover detection in Estonia from SAR images using machine learning methods
Date
2020
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
Usability of optical satellite data for monitoring snow cover can be limited in regions
with frequent high cloud coverage. Synthetic aperture radar (SAR) could theoretically
be used to monitor snow regardless of clouds or lack of illumination. There are several
factors that complicate the task in Estonia such as dense vegetation and quickly changing
snow conditions. So far most studies on using SAR for snow detection have been done
in mountainous regions and over short time periods. The aim of this study was to
test applicability of a method that combines most common features for snow detection
extracted from SAR images in a machine learning model. This method had shown good
transferability in mountain regions, however the modelling results on Estonian data were
unsatisfactory. Analysis of features derived from SAR images revealed poor separability
of snow free and snow covered classes. This suggest the main issue is with the feature
extraction methods rather than machine learning. Possibly the processing chain could be
optimized for Estonia and other regions with flat topography and predominantly dense
vegetation. This thesis did not result in a usable model, but could serve as a basis for
further studies.
Description
Keywords
synthetic aperture radar, backscattering, InSAR, PolSAR, snow classification, random forest