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 - Leppäranta, Matti
N1 - Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/10/31
Y1 - 2018/10/31
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, coherencemagnitude 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 backscatterintensity & 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, coherencemagnitude 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 backscatterintensity & 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 - Maximum likelihood
KW - Random forests
KW - Remote sensing
KW - Sea ice classification
KW - TanDEM-X
UR - http://www.scopus.com/inward/record.url?scp=85064192218&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2018.8518996
DO - 10.1109/IGARSS.2018.8518996
M3 - Conference article in proceedings
AN - SCOPUS:85064192218
SN - 978-1-5386-7151-1
T3 - IEEE International Geoscience and Remote Sensing Symposium Proceedings
SP - 7328
EP - 7331
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
PB - IEEE Institute of Electrical and Electronic Engineers
T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Y2 - 22 July 2018 through 27 July 2018
ER -