@inproceedings {290, title = {SEC: Stochastic ensemble consensus approach to unsupervised SAR sea-ice segmentation}, booktitle = {6th Canadian Conference on Computer and Robot Vision}, year = {2009}, month = {05/2009}, address = {Kelowna, British Columbia, Canada}, abstract = {

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, maximum likelihood (ML) classification is performed to estimate the final class of each pixel within the SAR sea-ice imagery based on the estimated GMM and the assigned sub-classes. SEC was tested using a variety of operational RADARSAT-1 and RADARSAT-2 SAR sea-ice imagery provided by the Canadian Ice Service (CIS) and was shown to produce successfully segmentation results that were superior to approaches based on K-means clustering, Gamma mixture models, and Markov Random Field (MRF) models for sea-ice segmentation.

}, keywords = {Canadian Ice Service, expectation maximization approach, Gaussian mixture model, GMM, image segmentation, K-means clustering, Markov Random Field models, maximum likelihood classification, maximum likelihood estimation, oceanographic techniques, polar regions, probability, probability distribution, RADARSAT-1 imagery, RADARSAT-2 imagery, random samples, remote sensing by radar, SAR sea-ice imagery, sea ice, sea-ice analysis, sea-ice monitoring, stochastic ensemble consensus approach, synthetic aperture radar, tonal characteristics, underlying ice type, unsupervised SAR sea-ice segmentation, weighted median consensus strategy}, doi = {http://dx.doi.org/10.1109/CRV.2009.25}, author = {A Wong and D A. Clausi and P Fieguth} } @article {419, title = {Unsupervised segmentation of synthetic aperture radar sea ice imagery using a novel Markov random field model}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {43}, year = {2005}, pages = {528 - 538}, chapter = {2940}, abstract = {

\—Environmental and sensor challenges pose difficulties for the development of computer-assisted algorithms to segment synthetic aperture radar (SAR) sea ice imagery. In this research, in support of operational activities at the Canadian Ice Service, images containing visually separable classes of either ice and water or multiple ice classes are segmented. This paper uses image intensity to discriminate ice from water and uses texture features to identify distinct ice types. In order to seamlessly combine image spatial relationships with various image features, a novel Bayesian segmentation approach is developed and applied. This new approach uses a function-based parameter to weight the two components in a Markov random field (MRF) model. The devised model allows for automatic estimation of MRF model parameters to produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to successfully segment various SAR sea ice images and achieve improvement over existing published methods including the standard MRF-based method, finite Gamma mixture model, and K-means clustering.

}, keywords = {Classification, coocurrence, expectation-maximization (EM), Gamma distribution, intensity, K-means clustering, Markov random field (MRF), mixture model, Pattern Recognition, probabilities, sea ice, Segmentation, synthetic aperture radar (SAR), texture, unsupervised}, author = {H Deng and D A. Clausi} } @article {621, title = {Unsupervised image segmentation using a simple MRF model with a new implementation scheme}, journal = {Pattern Recognition}, volume = {37}, year = {2004}, pages = {2323{\textendash}2335}, abstract = {

A simple Markov random field model with a new implementation scheme is proposed for unsupervised image segmentation based on image features. The traditional two-component MRF model for segmentation requires training data to estimate necessary model parameters and is thus unsuitable for unsupervised segmentation. The new implementation scheme solves this problem by introducing a function-based weighting parameter between the two components. Using this method, the simple MRF model is able to automatically estimate model parameters and produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to segment various types of images (gray scale, color, texture)and achieves an improvement over the traditional method.

}, keywords = {Color Image, Expectation-maximization (EM) algorithm, Feature space, image segmentation, K-means clustering, Markov random field (MRF), sea ice, synthetic aperture radar (SAR), Texture Image, unsupervised segmentation}, author = {H Deng and D A. Clausi} } @inproceedings {445, title = {Unsupervised segmentation of synthetic aperture radar sea ice imagery using MRF models}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, month = {05/2004}, address = {London, Ontario, Canada}, abstract = {

Due to both environmental and sensor reasons, it is challenging to develop computer-assisted algorithms to segment SAR (synthetic aperture radar) sea ice imagery. In this research, images containing either ice and water or multiple ice classes are segmented. This paper proposes to use the image intensity to discriminate ice from water and to use texture features to separate different ice types. In order to seamlessly combine spatial relationship information in an ice image with various image features, a novel Bayesian segmentation approach is developed Experiments demonstrate that the proposed algorithm is able to segment both types of sea ice images and achieves an improvement over the standard MRF (Markov random field) based method, the finite Gamma mixture model and the K-means clustering method.

}, keywords = {expectation-maximization (EM), Gamma distribution, image feature, image segmentation, K-means clustering, Markov random field (MRF), mixture model, sea ice, synthetic aperture radar (SAR), texture, unsupervised segmentation}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301420}, author = {H Deng and D A. Clausi} }