The article “Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements” has been accepted for publishing by IEEE Transactions on Geoscience and Remote Sensing. The  paper is a result of an ongoing collaboration between CIRFA and DLR researchers, Suman Singha (DLR), A. Malin Johansson (UiT), Nick Hughes (, Sine M. Hvidegaard (DTU Space), and Henriette Skourup (DTU Space).


In recent years spaceborne Synthetic Aperture Radar (SAR) polarimetry has become a valuable tool for sea ice analysis. Here we employ an automatic sea ice classification algorithm on two sets of spatially and temporally near coincident fully polarimetric acquisitions from the ALOS-2, Radarsat-2 and TerraSAR-X/TanDEM-X satellites. Overlapping coincident sea ice freeboard  measurements from Airborne Laser Scanner (ALS) data are used to validate the classification results. The automated sea ice classification algorithm consists of two steps. In the first step, we perform a polarimetric feature extraction procedure. Next the resulting feature vectors are ingested into a trained neural network classifier to arrive at a pixel-wise supervised classification. Coherency matrix based features which require an eigen-decomposition are found to be either of low relevance or redundant to other covariance matrix based features, which makes coherency matrix based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction). Classification results show that 100% of the open water is separated from the surrounding sea ice and that the sea ice classes have at least 96.9% accuracy. This analysis reveals analogous results for both X-band and C-band frequencies and slightly different for Lband. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected sea ice when compared with high resolution airborne measurements.


Singha, S. Johansson, M., Hughes, N., Hvidegaard, S.M., Skourup, H. (2018):  Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements. IEEE Transactions on Geoscience and Remote Sensing, 2018 [intranet]