DocumentCode
1207348
Title
Fusing AMSR-E and QuikSCAT Imagery for Improved Sea Ice Recognition
Author
Yu, Peter ; Clausi, David A. ; Howell, Stephen E L
Author_Institution
Dept. of Syst. Design Eng., Univ. of Waterloo, Waterloo, ON
Volume
47
Issue
7
fYear
2009
fDate
7/1/2009 12:00:00 AM
Firstpage
1980
Lastpage
1989
Abstract
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.
Keywords
geophysics computing; image classification; image fusion; oceanographic regions; principal component analysis; remote sensing; sea ice; AMSR-E-QuikSCAT imagery fusion; Advanced Microwave Scanning Radiometer for the Earth Observing System; PCA; Quick Scatterometer; Western Arctic region; data dimensionality reduction; maximum likelihood classifier; principal component analysis; sea ice mapping; sea ice recognition; supervised sea ice classification; training data reliability; Beaufort Sea; classification; data fusion; ice mapping; multisensor; passive microwave; principal component (PC) analysis (PCA); scatterometer;
fLanguage
English
Journal_Title
Geoscience and Remote Sensing, IEEE Transactions on
Publisher
ieee
ISSN
0196-2892
Type
jour
DOI
10.1109/TGRS.2009.2013632
Filename
4806106
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