Automated Ice–Water Classification Using Dual Polarization SAR Satellite Imagery

Abstract

Mapping ice and open water in ocean bodies is important for numerous purposes, including environmental analysis and ship navigation. The Canadian Ice Service (CIS) has stipulated a need for an automated ice-water discrimination algorithm using dual polarization images produced by RADARSAT-2. Automated methods can provide mappings in larger volumes, with more consistency, and in finer resolutions, which are otherwise impractical to generate. We have developed such an automated ice-water discrimination system called MAp-Guided Ice Classification. First, the HV (horizontal transmit polarization, vertical receive polarization) scene is classified using the “glocal” method, i.e., a hierarchical region-based classification method based on the published iterative region growing using semantics (IRGS) algorithm. Second, a pixel-based support vector machine (SVM) using a nonlinear radial basis function kernel classification is performed exploiting synthetic aperture radar gray-level cooccurrence texture and backscatter features. Finally, the IRGS and SVM classification results are combined using the IRGS approach but with a modified energy function to accommodate the SVM pixel-based information. The combined classifier was tested on 20 ground truthed dual polarization RADARSAT-2 scenes of the Beaufort Sea containing a variety of ice types and water patterns across melt, summer, and freeze-up periods. The average leave-one-out classification accuracy with respect to these ground truths is 96.42%, with a minimum of 89.95% for one scene. The MAGIC system is now under consideration by the CIS for operational use.