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Proceedings Paper

Comparison of SEM and linear unmixing approaches for classification of spectral data
Author(s): Scott G. Beaven; Lawrence E. Hoff; Edwin M. Winter
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Paper Abstract

In recent years a number of techniques for automated classification of terrain from spectral data have been developed and applied to multispectral and hyperspectral data. Use of these techniques for hyperspectral data has presented a number of technical and practical challenges. Here we present a comparison of two fundamentally different approaches to spectral classification of data: (1) Stochastic Expectation Maximization (SEM), and (2) linear unmixing. The underlying background clutter models for each are discussed and parallels between them are explored. Parallels are drawn between estimated parameters or statistics obtained from each type of method. The mathematical parallels are then explored through application of these clutter models to airborne hyperspectral data from the NASA AVIRIS sensor. The results show surprising similarity between some of the estimates derived from these two clutter models, despite the major differences in the underlying assumptions of each.

Paper Details

Date Published: 27 October 1999
PDF: 8 pages
Proc. SPIE 3753, Imaging Spectrometry V, (27 October 1999); doi: 10.1117/12.366292
Show Author Affiliations
Scott G. Beaven, Space and Naval Warfare Systems Ctr., San Diego (United States)
Lawrence E. Hoff, Hoff Engineering (United States)
Edwin M. Winter, Technical Research Associates, Inc. (United States)


Published in SPIE Proceedings Vol. 3753:
Imaging Spectrometry V
Michael R. Descour; Sylvia S. Shen, Editor(s)

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