@inproceedings {328,
	title = {Combining AMSR-E and QuikSCAT image data to improve sea ice classification},
	booktitle = {5th Workshop on Pattern Recognition for Remote Sensing},
	year = {2008},
	month = {12/2008},
	address = {Tampa, Florida, USA},
	abstract = {<p>The benefits of augmenting AMSR-E image data with QuikSCAT image data for supervised sea ice classification in the Western Arctic region are investigated. Experiments compared the performance of a maximum likelihood classifier when used with the AMSR-E only data set against the combined data and examined the preferred number of features to use as well as the reliability of training data over time. Adding QuikSCAT often improves classifier accuracy in a statistically significant manner and never decreased it significantly when enough features are used. Combining these data sets is beneficial for sea ice mapping. Using all available features is recommended and training data from a specific date remains reliable within 30 days.</p>
},
	keywords = {AMSR-E image data, classifier accuracy, hydrological techniques, image classification, maximum likelihood classifier, maximum likelihood estimation, QuikSCAT image data, sea ice, supervised sea ice classification, terrain mapping, Western Arctic region},
	doi = {http://dx.doi.org/10.1109/PRRS.2008.4783170},
	author = {P Yu and D A. Clausi and R De Abreu and T Agnew}
}
@inproceedings {490,
	title = {A general multiresolution approach to the estimation of dense fields in remote sensing},
	booktitle = {International Conference on Image Processing},
	year = {1996},
	address = {Lausanne, Switzerland},
	abstract = {<p>A fast multiscale optimal interpolation algorithm has been adapted to the mapping of hydrographic and other oceanographic data. This multiscale algorithm produces solution and error estimates which are consistent with those obtained from exact least-squares methods, but at a small fraction of the computational cost. Problems whose solution would be completely impractical using exact least-squares, that is problems with tens or hundreds of thousands of measurements and estimation grid points, can easily be solved on a small workstation using the multiscale algorithm. Contrary to methods previously proposed for solving large least-squares problems, the multiscale approach provides error statistics while permitting long-range correlations, using all measurements, and permitting arbitrary measurement locations</p>
},
	keywords = {arbitrary measurement locations, computational cost, correlation methods, dense fields estimation, error estimates, error statistics, exact least-squares methods, fast multiscale optimal interpolation algorithm, general multiresolution approach, geophysical signal processing, hydrographic data mapping, hydrological techniques, image resolution, interpolation, least squares approximations, long-range correlations, measurements, multiscale algorithm, oceanographic data mapping, oceanographic techniques, Remote Sensing, workstation},
	doi = {http://dx.doi.org/10.1109/ICIP.1996.560937},
	author = {P Fieguth and A S. Willsky and D Menemenlis and C Wunsch}
}
