Support Vector Machine-Based Endmember Extraction

Journal Article · Thu Jan 01 04:00:00 EST 2009 · Remote Sensing of Environment

DOI:https://doi.org/10.1109/TGRS.2008.2004708· OSTI ID:958814

Filippi, Anthony M ^[1]; Archibald, Richard K ^[1]

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1. ORNL

Introduced in this paper is the utilization of Support Vector Machines (SVMs) to automatically perform endmember extraction from hyperspectral data. The strengths of SVM are exploited to provide a fast and accurate calculated representation 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 dimensionality, and hence, the computational cost. Finally, endmember extraction for the whole data set is accomplished. Results indicate that this Support Vector Machine-Based Endmember Extraction (SVM-BEE) algorithm has the capability of autonomously determining endmembers from multiple clusters with computational speed and accuracy, while maintaining a robust tolerance to noise.

• Research Organization: Oak Ridge National Laboratory (ORNL); Center for Computational Sciences
• Sponsoring Organization: SC USDOE - Office of Science (SC)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 958814
• Journal Information: Remote Sensing of Environment, Journal Name: Remote Sensing of Environment Journal Issue: 3 Vol. 47; ISSN RSEEA7; ISSN 0034-4257
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
ACCURACY
ALGORITHMS
COMPUTERS
DATA PROCESSING
DISTRIBUTION
TOLERANCE
VECTORS