@article {125, title = {Unsupervised operational SAR sea ice image classification}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {50}, year = {2012}, pages = {4397-4408}, abstract = {

Thousands of spaceborne synthetic aperture radar (SAR) sea-ice images are systematically processed every year in support of operational activities such as ship navigation and environmental monitoring. An automated approach that generates a pixel-level sea-ice image classification is required since manual pixel-level classification is not feasible. Currently, using a standardized approach, trained ice analysts manually segment full SAR scenes into smaller polygons to record ice types and concentrations. Using this data, pixel-level classification can be achieved by initial unsupervised segmentation of each polygon followed by automatic sea-ice labeling of the full scene. A fully automated Markov random field model that is used to assign labels to all segmented regions in the full scene has been designed and implemented. This approach is the first known successful end-to-end process for operational SAR sea-ice image classification. In addition, a novel performance evaluation framework has been developed to validate the segmentation and labeling of SAR sea-ice images. A trained sea-ice expert has conducted an arms length evaluation using this framework to generate a set of full scene reference images used for testing. Testing demonstrates operational success of the labeling approach.

}, keywords = {image classification, Markov random field (MRF), sea-ice, Synthetic, unsupervised segmentation}, author = {S Ochilov 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} }