Automated Ice-Water Classification Using Dual Polarization SAR Satellite Imagery

• Published in: IEEE transactions on geoscience and remote sensing 2014-09, Vol.52 (9), p.5529-5539
• Main Authors: Leigh, Steven, Zhijie Wang, Clausi, David A.
• Format: Article
• Language: English
• Subjects: Algorithms; Automated; Automation; Backscatter; Classification; Computational modeling; gray-level cooccurrence matrix (GLCM); Ice; iterative region growing using semantics (IRGS); Kernel; Mapping; Marine; Polarization; Radar; RADARSAT-2; Satellites; sea ice; support vector machine (SVM); Support vector machines; Synthetic aperture radar; synthetic aperture radar (SAR); Texture
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2013.2290231

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.