@article {383,
	title = {Filament preserving model (FPM) segmentation applied to SAR sea-ice imagery},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {44},
	year = {2006},
	pages = {3687 - 3694},
	abstract = {<p>Modeling spatial context constraints using a Markov random field (MRF) has been widely used in the segmentation of noisy images. Its applicability to synthetic aperture radar (SAR) sea-ice segmentation has also been demonstrated recently. However, most existing MRF models are not capable of preserving filaments, specifically leads and ridges for SAR sea ice, which are valuable for ship navigation applications and necessary for identifying certain ice types. In this paper, a new statistical context model is proposed that, within the same scene, can simultaneously preserve narrow elongated features while producing similar smooth segmentation results comparable to typical MRF-based approaches. Tested on one synthetic image and two SAR sea-ice scenes, this filament preserving model substantially improves classification accuracies when compared to standard Gaussian mixture and MRF-based segmentation algorithms</p>
},
	keywords = {egg code, filament preserving model, image classification, image segmentation, Markov processes, Markov random field, MRF-based segmentation algorithm, noisy image segmentation, oceanographic techniques, remote sensing by radar, SAR sea-ice imagery, sea ice, ship navigation, standard Gaussian mixture, synthetic aperture radar, synthetic image},
	issn = {0196-2892},
	doi = {http://dx.doi.org/10.1109/TGRS.2006.885046},
	author = {Q Yu and D A. Clausi}
}
@inproceedings {393,
	title = {Filament preserving segmentation for SAR sea ice imagery using a new statistical model},
	booktitle = {18th International Conference on Pattern Recognition (ICPR)},
	year = {2006},
	month = {08/2006},
	address = {Hong Kong},
	abstract = {<p>Modelling spatial context constraints using Markov random field (MRF) has been widely used in the segmentation of noisy images. Its applicability to SAR sea ice segmentation has also been demonstrated by Deng and Clausi (2005). However, most existing MRF models are not capable of preserving filaments, specifically leads and ridges for SAR sea ice, which are valuable for ship navigation applications and helpful for identifying certain ice types. A new statistical context model is proposed that can preserve such narrow elongated features while producing similar smooth segmentation results as those of existing MRF based approaches</p>
},
	keywords = {Feature Extraction, filament preserving segmentation, geophysical signal processing, Image Denoising, image segmentation, Markov processes, Markov random field, narrow elongated features, noisy image segmentation, oceanographic techniques, radar imaging, SAR sea ice imagery, sea ice, ship navigation, spatial context constraints, statistical analysis, statistical model, synthetic aperture radar},
	doi = {http://dx.doi.org/10.1109/ICPR.2006.561},
	author = {Q Yu and D A. Clausi}
}
