|Title||Automated ice-water classification using dual polarization SAR satellite imagery|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Leigh, S., Z. Wang, and D. A. Clausi|
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|Keywords||Classification, grey-level co-occurerence matrix (GLCM), IRGS, RADARSAT-2, sea ice, support vector machine (SVM), synthetic aperture radar (SAR)|
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 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 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 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.