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 anal-ysis and ship navigation. The Canadian Ice Service (CIS) has stipulated a need for an automated ice-water discrimination algo-rithm using dual polarization images produced by RADARSAT-...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Text |
| Language: | English |
| Subjects: | |
| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.651.8535 http://vip.uwaterloo.ca/files/publications/manuscript_two_column.pdf |
| Summary: | Abstract—Mapping ice and open water in ocean bodies is important for numerous purposes including environmental anal-ysis and ship navigation. The Canadian Ice Service (CIS) has stipulated a need for an automated ice-water discrimination algo-rithm using dual polarization images produced by RADARSAT-2. Automated methods can provide mappings in larger volumes, with more consistency, and in finer resolutions that are otherwise impractical to generate. We have developed such an automated ice-water discrimination system called MAGIC. First, the HV (horizontal transmit polarization, vertical receive polarization) scene is classified using the “glocal ” method, a hierarchical region-based classification method based on the published itera-tive 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 grey-level co-occurrence 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 CIS for operational use. |
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