@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}
}
@inproceedings {405,
	title = {Pixel-based sea ice classification using the MAGSIC system},
	booktitle = {International Society for Photogrammetry and Remote Sensing},
	year = {2006},
	address = {Enschede, The Netherlands},
	abstract = {<p>MAGSIC is an operation-oriented system in development dedicated to map-guided classification of sea ice for navigation route planning and meteorological modeling. It has already produced promising results in difficult situations such as the Gulf of Saint-Lawrence in late winter. The Canadian Ice Service (CIS) produces ice maps made of large regions with relatively homogeneous concentrations of different ice types. MAGSIC uses the information of these maps to produce a pixel-based (rather than region-based) ice map by labeling a Markov random field (MRF) segmentation of RADARSAT-1 data along with its derived texture features. The system uses a novel implementation of MRF segmentation in combination with a unique labeling approach based on \&ldquo;cognitive reasoning\&rdquo;. Although reasonably successful, the system often had difficulties identifying ice type that required cues based on the shape recognition of large ice floes or leads. This article aims at thoroughly testing the MAGSIC system using validation data acquired during the \&ldquo;2003 Gulf of Saint-Lawrence SAR Validation Field Program\&rdquo; performed by CIS. Some new features were also added to MAGSIC and were evaluated. Results suggest a reasonable success and that the errors can be partially attributed to the generalized nature of the analysts ́interpretation and to the difficulties of obtaining concurring ground and image data. They suggest that classification metrics that can compare sample distributions were slightly superior for labeling purposes but this could not be confirmed statistically.</p>
},
	keywords = {Classification, navigation, Radarsat, Reasoning, Segmentation, Snow Ice, texture},
	author = {P Maillard and D A. Clausi}
}
