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Proceedings Paper

Evaluation of endmember selection techniques and performance results from ORASIS hyperspectral analysis
Author(s): Kwok Yeung Tsang; John M. Grossmann; Peter J. Palmadesso; John A. Antoniades; Mark M. Baumback; Jeffrey H. Bowles; Mark Daniel; John Fisher; Daniel Haas
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Paper Abstract

In this work, we generate ROC curves on real and synthetic scenes and develop scoring methods to evaluate the performance of the ORASIS hyperspectral algorithm. The goal of this effort is to improve the overall performance of ORASIS, focusing on the endmember selection methods. ROC curve evaluations have been performed on hyperspectral data sets from different scenes. We have scored by target and by target pixel. A scene generator has been developed allowing many features: combination of real or synthetic background and multiple, distinct targets; user-defined angle of target spectrum to background subspace; and user-specified non-uniform target/background transparency.

Paper Details

Date Published: 2 July 1998
PDF: 8 pages
Proc. SPIE 3372, Algorithms for Multispectral and Hyperspectral Imagery IV, (2 July 1998); doi: 10.1117/12.312607
Show Author Affiliations
Kwok Yeung Tsang, Naval Research Lab. and Science Applications International Corp. (United States)
John M. Grossmann, Naval Research Lab. (United States)
Peter J. Palmadesso, Naval Research Lab. (United States)
John A. Antoniades, Naval Research Lab. (United States)
Mark M. Baumback, Naval Research Lab. (United States)
Jeffrey H. Bowles, Naval Research Lab. (United States)
Mark Daniel, Naval Research Lab. and Science Applications International Corp. (United States)
John Fisher, Naval Research Lab. (United States)
Daniel Haas, Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 3372:
Algorithms for Multispectral and Hyperspectral Imagery IV
Sylvia S. Shen; Michael R. Descour, Editor(s)

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