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• W2113941175 abstract "This study examined the applicability of data fusion and classifier ensemble techniques for vegetation mapping in the coastal Everglades. A framework was designed to combine these two techniques. In the framework, 20-m hyperspectral imagery collected from Airborne Visible/Infrared Imaging Spectrometer was first merged with 1-m Digital Orthophoto Quarter Quads using a proposed pixel/feature-level fusion strategy. The fused data set was then classified with an ensemble approach based on two contemporary machine learning algorithms: Random Forest and Support Vector Machine. The framework was applied to classify nine vegetation types in a portion of the coastal Everglades. An object-based vegetation map was produced with an overall accuracy of 90% and Kappa value of 0.86. Per-class classification accuracy varied from 61% for identifying buttonwood forest to 100% for identifying red mangrove scrub. The result shows that the framework is promising for automated vegetation mapping in the Everglades." @default.
• W2113941175 created "2016-06-24" @default.
• W2113941175 creator A5035662169 @default.
• W2113941175 creator A5047308968 @default.
• W2113941175 date "2013-02-06" @default.
• W2113941175 modified "2023-10-16" @default.
• W2113941175 title "Data fusion and classifier ensemble techniques for vegetation mapping in the coastal Everglades" @default.
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• W2113941175 doi "https://doi.org/10.1080/10106049.2012.756940" @default.
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