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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 3, MARCH 2009 771 Support Vector Machine-Based
 

Summary: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 3, MARCH 2009 771
Support Vector Machine-Based
Endmember Extraction
Anthony M. Filippi and Rick Archibald
Abstract--Introduced in this paper is the utilization of support
vector machines (SVMs) to semiautomatically perform endmem-
ber extraction from hyperspectral data. The strengths of SVM
are exploited to provide a fast and accurate calculated represen-
tation of high-dimensional data sets that may consist of multiple
distributions. Once this representation is computed, the number
of distributions can be determined without prior knowledge. For
each distribution, an optimal transform can be determined that
preserves informational content while reducing the data dimen-
sionality and, hence, the computational cost. Finally, endmember
extraction for the whole data set is accomplished. Results indicate
that this SVM-based endmember extraction algorithm has the
capability of semiautonomously determining endmembers from
multiple clusters with computational speed and accuracy while
maintaining a robust tolerance to noise.
Index Terms--Endmember extraction, hyperspectral imaging,

  

Source: Archibald, Richard - Computer Science and Mathematics Division, Oak Ridge National Laboratory

 

Collections: Computer Technologies and Information Sciences