Automated SEA ICE Classification Over the Baltic SEA using Multiparametric Features of Tandem-X Insar Images

Marjan Marbouti*, Oleg Antropov, Patrick Eriksson, Jaan Praks, Vahid Arabzadeh, Eero Rinne, Matti Lepparanta

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)


In this study, bistatic interferometric Synthetic Aperture Radar (InSAR) data acquired by the TanDEM-X mission were used for automated classification of sea ice over the Baltic Sea, in the Bothnic Bay. A scene acquired in March of 2012 was used in the study. Backscatter-intensity, coherence-magnitude and InSAR-phase, as well as their different combinations, were used as informative features in several classification approaches. In order to achieve the best discrimination between open water and several sea ice types (new ice, thin smooth ice, close ice, very close ice, ridged ice, heavily ridged ice and ship-track), Random Forests (RF) and Maximum likelihood (ML) classifiers were employed. The best overall accuracies were achieved using combination of backscatter-intensity & InSAR-phase and backscatter-intensity & coherence-magnitude, and were 76.86% and 75.81% with RF and ML classifiers, respectively. Overall, the combination of backscatter-intensity & InSAR-phase with RF classifier was suggested due to the highest overall accuracy (OA) and smaller computing time in comparison to ML. In contrast to several earlier studies, we were able to discriminate water and the thin smooth ice.

Original languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Number of pages4
ISBN (Print)978-1-5386-7150-4
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventInternational Geoscience and Remote Sensing Symposium - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018
Conference number: 38

Publication series

NameIEEE International Symposium on Geoscience and Remote Sensing IGARSS
ISSN (Print)2153-6996


ConferenceInternational Geoscience and Remote Sensing Symposium
Abbreviated titleIGARSS
Internet address


  • Remote sensing
  • sea ice classification
  • random forests
  • Maximum likelihood
  • TanDEM-X
  • C-BAND


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