@inproceedings {287,
	title = {IceSynth: An image synthesis system for sea-ice segmentation evaluation},
	booktitle = {6th Canadian Conference on Computer and Robot Vision},
	year = {2009},
	month = {05/2009},
	address = {Kelowna, British Columbia, Canada},
	abstract = {<p>An ongoing challenge in automatic sea-ice monitoring using synthetic aperture radar (SAR) is the automatic segmentation of SAR sea-ice images based on the underlying ice type. Given the intractability of obtaining ground-truth segmentation data from polar regions, the evaluation of automatic SAR sea-ice image segmentation algorithms is generally limited to tests using real SAR imagery based on pseudo-ground truth data (e.g., manual segmentations) and simple synthetic tests using basic shape primitives. As such, it is difficult to evaluate automatic segmentation algorithms in a systematic and reliable manner using realistic scenarios. To tackle this issue, a novel image synthesis system named IceSynth is presented, which is capable of generating a variety of synthetic sea-ice images that are representative of real SAR sea-ice imagery. In IceSynth, SAR sea-ice textures for each ice type are synthesized via stochastic sampling based on non-parametric local conditional texture probability distribution estimates. A stochastic sampling approach based on non-parametric local class probability distribution estimates is used to generate large-scale sea-ice structures of various ice types based on ice classification priors extracted from real SAR sea-ice imagery. Experimental results show that IceSynth is capable of generating realistic-looking SAR sea-ice images that are well-suited for performing objective evaluation of SAR sea-ice image segmentation algorithms.</p>
},
	keywords = {automatic sea-ice monitoring, ice classification, ice types, IceSynth, image classification, image segmentation, image synthesis system, image texture, oceanographic techniques, polar regions, probability, probability distribution estimation, pseudo-ground truth data, remote sensing by radar, SAR imagery, sea ice, sea-ice segmentation evaluation, sea-ice structures, sea-ice textures, stochastic processes, stochastic sampling, synthetic aperture radar},
	doi = {http://dx.doi.org/10.1109/CRV.2009.27},
	author = {A Wong and W Zhang and D A. Clausi}
}
@article {813,
	title = {A comparison of two 85-GHz SSM/I ice concentration algorithms with AVHRR and ERS-2 SAR imagery},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {41},
	year = {2003},
	pages = {2294 - 2306},
	abstract = {<p>Sea ice concentrations obtained with two algorithms from Special Sensor Microwave/Imager (SSM/I) data are compared to spaceborne visible/infrared and active microwave imagery for the Greenland Sea in spring. Both algorithms, the ARTIST Sea Ice algorithm (ASI) and the SEA LION algorithm (SLA), utilize 85-GHz SSM/I brightness temperatures with a spatial resolution of 15 km times;13 km. Ice concentrations obtained from Advanced Very High Resolution Radiometer (AVHRR) infrared data in cloud-free areas are underestimated by SLA and ASI ice concentrations by 3.6\% and 8.3\% (correlation coefficients of 0.90 and 0.91). Ice concentrations estimated from texture classified ERS-2 synthetic aperture radar (SAR) images by assigning experience-based ice concentrations to ice-type classes are overestimated by SLA and ASI ice concentrations by 4.4\% and 1.5\% (correlation coefficients of 0.84 and 0.77). However, omitting low/high ice concentrations forming up to 80\% (AVHRR) and 60\% (SAR) of the entire dataset reveals a significantly different statistic. For instance, the correlation between AVHRR and SLA and ASI ice concentrations drops to 0.77 and 0.70, respectively. All presented techniques to obtain ice concentrations need improvement and future developments should involve larger datasets. However, with care, both algorithms can be used to obtain reasonable ice concentration maps with a 12.5 km times;12.5 km grid-cell size.</p>
},
	keywords = {85 GHz, Advanced Very High Resolution Radiometer data, ARTIST sea ice algorithm, ASI, AVHRR imagery, brightness temperatures, Greenland Sea, ice-type classes, oceanographic techniques, radar imaging, remote sensing by radar, SAR imagery, sea ice, SEA LION algorithm, SLA, spaceborne imagery, spaceborne radar, Special Sensor Microwave/Imager data, Spring, SSM/I ice concentration algorithms, synthetic aperture radar, texture},
	issn = {0196-2892},
	doi = {http://dx.doi.org/10.1109/TGRS.2003.817181},
	author = {S Kern and L Kaleschke and D A. Clausi}
}
