@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 = {<p>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.</p>
},
	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}
}
@article {471,
	title = {A fast method to determine co-occurrence texture features},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {36},
	year = {1998},
	pages = {298 - 300},
	abstract = {<p>A critical shortcoming of determining texture features derived from grey-level co-occurrence matrices (GLCM\&$\#$39;s) is the excessive computational burden. This paper describes the implementation of a linked-list algorithm to determine co-occurrence texture features far more efficiently. Behavior of common co-occurrence texture features across difference grey-level quantizations is investigated</p>
},
	keywords = {co-occurrence texture feature, fast method, Feature Extraction, geophysical measurement technique, geophysical signal processing, geophysical techniques, grey-level co-occurrence matrices, grey-level quantization, image feature, image processing, image texture, land surface, linked-list algorithm, occurrence matrix, Remote Sensing, terrain mapping},
	doi = {http://dx.doi.org/10.1109/36.655338},
	author = {D A. Clausi and E Jernigan}
}
