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

doi:10.1109/IGARSS.2018.8518996

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 proceeding › Conference article in proceedings › Scientific › peer-review

3 Citations (Scopus)

Abstract

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 language	English
Title of host publication	2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Publisher	IEEE
Pages	7328-7331
Number of pages	4
ISBN (Print)	978-1-5386-7150-4
DOIs	https://doi.org/10.1109/IGARSS.2018.8518996
Publication status	Published - 2018
MoE publication type	A4 Conference publication
Event	IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018 Conference number: 38 https://www.igarss2018.org/

Publication series

Name	IEEE International Symposium on Geoscience and Remote Sensing IGARSS
Publisher	IEEE
ISSN (Print)	2153-6996

Conference

Conference	IEEE International Geoscience and Remote Sensing Symposium
Abbreviated title	IGARSS
Country/Territory	Spain
City	Valencia
Period	22/07/2018 → 27/07/2018
Internet address	https://www.igarss2018.org/

Keywords

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

Access to Document

10.1109/IGARSS.2018.8518996

http://hdl.handle.net/10138/311622

Cite this

Marbouti, M., Antropov, O., Eriksson, P., Praks, J., Arabzadeh, V., Rinne, E., & Lepparanta, M. (2018). Automated SEA ICE Classification Over the Baltic SEA using Multiparametric Features of Tandem-X Insar Images. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 7328-7331). Article 8518996 (IEEE International Symposium on Geoscience and Remote Sensing IGARSS). IEEE. https://doi.org/10.1109/IGARSS.2018.8518996

@inproceedings{9dca79fe53694402a536b7c2743e631b,

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

abstract = "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.",

keywords = "Remote sensing, sea ice classification, random forests, Maximum likelihood, TanDEM-X, C-BAND",

author = "Marjan Marbouti and Oleg Antropov and Patrick Eriksson and Jaan Praks and Vahid Arabzadeh and Eero Rinne and Matti Lepparanta",

year = "2018",

doi = "10.1109/IGARSS.2018.8518996",

language = "English",

isbn = "978-1-5386-7150-4",

series = "IEEE International Symposium on Geoscience and Remote Sensing IGARSS",

publisher = "IEEE",

pages = "7328--7331",

booktitle = "2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings",

address = "United States",

note = "IEEE International Geoscience and Remote Sensing Symposium , IGARSS ; Conference date: 22-07-2018 Through 27-07-2018",

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Marbouti, M, Antropov, O, Eriksson, P, Praks, J , Arabzadeh, V, Rinne, E & Lepparanta, M 2018, Automated SEA ICE Classification Over the Baltic SEA using Multiparametric Features of Tandem-X Insar Images. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings., 8518996, IEEE International Symposium on Geoscience and Remote Sensing IGARSS, IEEE, pp. 7328-7331, IEEE International Geoscience and Remote Sensing Symposium , Valencia, Spain, 22/07/2018. https://doi.org/10.1109/IGARSS.2018.8518996

Automated SEA ICE Classification Over the Baltic SEA using Multiparametric Features of Tandem-X Insar Images. / Marbouti, Marjan; Antropov, Oleg; Eriksson, Patrick et al.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. IEEE, 2018. p. 7328-7331 8518996 (IEEE International Symposium on Geoscience and Remote Sensing IGARSS).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Automated SEA ICE Classification Over the Baltic SEA using Multiparametric Features of Tandem-X Insar Images

AU - Marbouti, Marjan

AU - Antropov, Oleg

AU - Eriksson, Patrick

AU - Praks, Jaan

AU - Arabzadeh, Vahid

AU - Rinne, Eero

AU - Lepparanta, Matti

N1 - Conference code: 38

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Remote sensing

KW - sea ice classification

KW - random forests

KW - Maximum likelihood

KW - TanDEM-X

KW - C-BAND

U2 - 10.1109/IGARSS.2018.8518996

DO - 10.1109/IGARSS.2018.8518996

M3 - Conference article in proceedings

SN - 978-1-5386-7150-4

T3 - IEEE International Symposium on Geoscience and Remote Sensing IGARSS

SP - 7328

EP - 7331

BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings

PB - IEEE

T2 - IEEE International Geoscience and Remote Sensing Symposium

Y2 - 22 July 2018 through 27 July 2018

ER -

Marbouti M, Antropov O, Eriksson P, Praks J , Arabzadeh V, Rinne E et al. Automated SEA ICE Classification Over the Baltic SEA using Multiparametric Features of Tandem-X Insar Images. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. IEEE. 2018. p. 7328-7331. 8518996. (IEEE International Symposium on Geoscience and Remote Sensing IGARSS). doi: 10.1109/IGARSS.2018.8518996

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

Abstract

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Keywords

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