1 Fusing AMSR-E and QuikSCAT imagery for improved sea ice recognition
Abstract—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 using the combin...
| Main Authors: | , , , |
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| Format: | Text |
| Language: | English |
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.158.2125 http://www.eng.uwaterloo.ca/~dclausi/Papers/Published%202009/amsrqs_main.pdf |
| Summary: | Abstract—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 using the combined data. The preferred number of bands to use for classification was examined, as well as whether principal components analysis 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 decreased 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 principal components analysis does not offer any benefit for these data and training data from a specific date remains reliable within 30 days. Index Terms—data fusion, classification, scatterometer, passive microwave, Beaufort Sea, ice mapping, multisensor, principal |
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