Potential of compact polarimetry for operational sea ice monitoring over Arctic and Antarctic Region

Abstract

SAR Polarimetry has become a valuable tool in spaceborne SAR based sea ice analysis. The two major objectives in SAR based remote sensing of sea ice is on the one hand to have a large coverage of the imaged ground area, and on the other hand to obtain a radar response that carries as much information as possible. Whereas single-polarimetric acquisitions of existing sensors offer a wide coverage on the ground, dual polarimetric, or even better fully polarimetric data offer a higher information content which allows for a more reliable automated sea ice analysis. In order to reconcile the advantages of fully polarimetric acquisitions with the higher ground coverage of acquisitions with fewer polarimetric channels, compact polarimetric acquisitions offer a trade-off between the mentioned objectives. With the advent of the RISAT-1 satellite platform, we are able to explore the potential of compact polarimteric acquisitions for sea ice analysis and classification in operational environment. Our algorithmic approach for an automated sea ice classification consists of two steps. In the first step, we perform a feature extraction procedure. The resulting feature vectors are then ingested into a trained neural network classifier to arrive at a pixelwise supervised classification. We present our results on datasets acquired over both Arctic and Antarctic sea ice.