MATLAB PROJECT
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.
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