@inproceedings {238,
	title = {Automated classification of operational SAR sea ice images},
	booktitle = {7th Canadian Conference on Computer and Robot Vision},
	year = {2010},
	month = {06/2010},
	address = {Ottawa, Ontario, Canada},
	abstract = {<p>The automated classification of operational sea ice satellite imagery is important for ship navigation and environmental monitoring. Annually, thousands of large synthetic aperture radar (SAR) scenes are manually processed by the Canadian Ice Service (CIS) and pixel-level interpretation is not feasible. Trained ice analysts divide SAR images into $\#$x201D;polygon $\#$x201D; areas and then identify the number and type of ice classes per polygon. Full scene unsupervised classification can be performed by first segmenting each polygon into distinct regions algorithmically. Since there is insufficient information to assign a sea ice label for each region within an individual polygon, a Markov random field formulation using joint information to label each region in a full SAR scene has been developed. This approach has been successfully applied to operational CIS data to produce pixel-level classified images and is the first known successful end-to-end process for automatically classifying operational SAR sea ice images.</p>
},
	keywords = {automated classification, Canadian Ice Service, environmental monitoring, geophysical image processing, image segmentation, Markov processes, Markov random field, navigation, operational SAR sea ice images, pattern classification, pixel level interpretation, sea ice, sea ice satellite imagery, ship navigation, synthetic aperture radar, synthetic aperture radar scenes, unsupervised classification},
	doi = {http://dx.doi.org/10.1109/CRV.2010.59},
	author = {S Ochilov and D A. Clausi}
}
@article {319,
	title = {An evaluation of RADARSAT-1 and ASTER data for mapping veredas (palm swamps)},
	journal = {Sensors},
	year = {2008},
	pages = {6055-6076},
	abstract = {<p>Veredas (palm swamps) are wetland complexes associated with the Brazilian savanna ( cerrado ) that often represent the only available source of water for the ecosystem during the dry months. Their extent and condition a re mainly unknown and their cartography is an essential issue for their protect ion. This research article evaluates some of the fine resolution satellite data both in the rada r (Radarsat-1) and optical domain (ASTER) for the delineation and characterization of veredas . Two separate approaches are evaluated. First, given the known potential of Radarsat-1 imag es for wetland inventories, the automatic delineation of veredas is tested using only Radarsat-1 data and a Markov random fields region-based segmentation. In this case, to increase performance, processing is limited to a buffer zone around the river network. Then, characterization of their type is attempted using traditional classification methods of ASTER optical data combined with Radarsat-1 data. The automatic classification of Ra darsat data yielded results with an overall accuracy between 62 and 69\%, that proved reliable enough for delineating wide and very humid veredas . Scenes from the wet season and with a smaller ang le of incidence systematically yielded better results. For the clas sification of the main vegetation types, better results (overall success of 78.8\%) were obta ined by using only the visible and near infrared (VNIR) bands of the ASTER image. Radarsat data did not bring any improvement to these classification results. In fact, when usin g solely the Radarsat data from two different angle of incidence and two different dates, the classification results were low</p>
},
	keywords = {ASTER, Markov Random Fields, Palm swaps, Radarsat, Supervised Classification, unsupervised classification, Vegetation types, Wetlands},
	author = {P Maillard and R Alencar-Silva and D A. Clausi}
}
