Robustness of SAR Sea Ice Type classification across incidence angles and seasons at L-band

In recent years, space-borne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice type retrieval. L-band SAR has proven to be sensitive toward deformed sea ice and is complementary compared with operationally used C-band SAR for sea ice type classification during the ear...

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Bibliographic Details
Published in:IEEE Transactions on Geoscience and Remote Sensing
Main Authors: Singha, Suman, Johansson, Malin, Doulgeris, Anthony Paul
Format: Article in Journal/Newspaper
Language:English
Published: IEEE 2020
Subjects:
VDP::Technology: 500::Information and communication technology: 550
VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550
Arctic
Sea ice
Online Access:https://hdl.handle.net/10037/20244
https://doi.org/10.1109/TGRS.2020.3035029
Description
Summary:In recent years, space-borne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice type retrieval. L-band SAR has proven to be sensitive toward deformed sea ice and is complementary compared with operationally used C-band SAR for sea ice type classification during the early and advanced melt seasons. Here, we employ an artificial neural network (ANN)-based sea ice type classification algorithm on a comprehensive data set of ALOS-2 PALSAR-2 fully polarimetric images acquired with a range of incidence angles and during different environmental conditions. The variability within the data set means that it is ideal for making a novel assessment of the robustness of the sea ice classification, investigating the intraclass variability, the seasonal variations, and the incidence angle effect on the sea ice classification results. The images coincide with two different Arctic campaigns in 2015: the Norwegian Young Sea Ice Cruise 2015 (N-ICE2015) and the Polarstern's (PS92) Transitions in the Arctic Seasonal Sea Ice Zone (TRANSSIZ). We find that it is essential to take into account seasonality and intraclass variability when establishing training data for machine learning-based algorithms though moderate differences in incidence angle are possible to accommodate by the classifier during the dry and cold winter season. We also conclude that the incidence angle dependence of backscatter for a given ice type is consistent for different Arctic regions.

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