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Proceedings Paper

Support vector machines for hyperspectral remote sensing classification
Author(s): J. Anthony Gualtieri; Robert F. Cromp
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Paper Abstract

The Support Vector Machine provides a new way to design classification algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a difficult classification problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class problem respectively. These results are somewhat better than other recent result on the same data. A key feature of this classifier is its ability to use high-dimensional data without the usual recourse to a feature selection step to reduce the dimensionality of the data. For this application, this is important, as hyperspectral data consists of several hundred contiguous spectral channels for each exemplar. We provide an introduction to this new approach, and demonstrate its application to classification of an agriculture scene.

Paper Details

Date Published: 29 January 1999
PDF: 12 pages
Proc. SPIE 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition, (29 January 1999); doi: 10.1117/12.339824
Show Author Affiliations
J. Anthony Gualtieri, NASA Goddard Space Flight Ctr. (United States)
Robert F. Cromp, NASA Goddard Space Flight Ctr. (United States)


Published in SPIE Proceedings Vol. 3584:
27th AIPR Workshop: Advances in Computer-Assisted Recognition
Robert J. Mericsko, Editor(s)

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