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SEC: Stochastic Ensemble Consensus Approach to Unsupervised SAR Sea-Ice Segmentation

9 years 8 months ago
SEC: Stochastic Ensemble Consensus Approach to Unsupervised SAR Sea-Ice Segmentation
The use of synthetic aperture radar (SAR) has become an integral part of sea-ice monitoring and analysis in the polar regions. An important task in sea-ice analysis is to segment SAR sea-ice imagery based on the underlying ice type, which is a challenging task to perform automatically due to various imaging and environmental conditions. A novel stochastic ensemble consensus approach to sea-ice segmentation (SEC) is presented to tackle this challenging task. In SEC, each pixel in the SAR sea-ice image is assigned an initial sub-class based on its tonal characteristics. Ensembles of random samples are generated from a random field representing the SAR sea-ice imagery. The generated ensembles are then used to re-estimate the sub-class of the pixels using a weighted median consensus strategy. Based on the probability distribution of the sub-classes, an expectation maximization (EM) approach is utilized to estimate the final class likelihoods using a Gaussian mixture model (GMM). Finally...
Alexander Wong, David A. Clausi, Paul W. Fieguth
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where CRV
Authors Alexander Wong, David A. Clausi, Paul W. Fieguth
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