Contextual Classification of Sea-Ice Types Using Compact Polarimetric SAR Data

Abstract

Automatic classification methods using satellite imagery are beneficial in the sea-ice-type mapping of the Arctic regions. In the near future, the RADARSAT Constellation Mission (RCM) will be launched, providing unique compact polarimetric (CP) synthetic aperture radar (SAR) data, expected to be an improvement over the current RADARSAT-2 dual-polarimetric SAR imagery. This motivates the implementation of a CP-dedicated automatic scene classification approach. First, an existing unsupervised segmentation algorithm called iterative region growing using semantics (IRGS) is used to segment ice-class homogeneous regions to reduce the impact of speckle noise. Second, a support vector machine (SVM) is used to classify the ice-type labels for each homogeneous region. Two complex quad-polarimetric RADARSAT-2 scenes are used to mathematically simulate the corresponding CP scenes for algorithm testing. Classification accuracy shows that using only the two CP intensity images leads to improved results compared with standard dual-polarimetric scenes. Using the CP data, the best classification results are obtained with the reconstructed QP data for the IRGS segmentation and all derived CP features for the SVM labeling. The results support the expected potential that CP scenes will provide improved sea-ice classification than the current operational dual-pol scenes.