@article {271,
	title = {Fusing AMSR-E and QuikSCAT imagery for improved sea ice recognition},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {47},
	year = {2010},
	pages = {1980 - 1989},
	abstract = {<p>The benefits of augmenting Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) image data with Quick Scatterometer (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 using the combined data. The preferred number of bands to use for classification was examined, as well as whether principal component analysis (PCA) can be used to reduce the dimensionality of the data. The reliability of training data over time was also investigated. Adding QuikSCAT often improves classifier accuracy in a statistically significant manner and never decreases it significantly when a sufficient number of bands are used. Combining these data sets is beneficial for sea ice mapping. Using all available bands is recommended, data fusion with PCA does not offer any benefit for these data, and training data from a specific date remains reliable within 30 days.</p>
},
	keywords = {Advanced Microwave Scanning Radiometer for the Earth Observing System, AMSR-E-QuikSCAT imagery fusion, data dimensionality reduction, geophysics computing, image classification, image fusion, maximum likelihood classifier, oceanographic regions, PCA, principal component analysis, Quick Scatterometer, Remote Sensing, sea ice, sea ice mapping, sea ice recognition, supervised sea ice classification, training data reliability, Western Arctic region},
	doi = {http://dx.doi.org/10.1109/TGRS.2009.2013632},
	author = {P Yu and D A. Clausi and S Howell}
}
@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}
}
