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...

• Type: Conference
• Year: 2009
• Where: CRV
• Authors: Alexander Wong, David A. Clausi, Paul W. Fieguth
• Keywords: CRV 2009, Robotics, SAR Sea-ice, SAR Sea-ice Imagery, Sea-ice