Evaluation of endmember selection techniques and performance results from ORASIS hyperspectral analysis

Kwok Yeung Tsang; John M. Grossmann; Peter J. Palmadesso; John A. Antoniades; Mark M. Baumback; Jeffrey H. Bowles; Mark Daniel; John Fisher; Daniel Haas

doi:10.1117/12.312607

2 July 1998 Evaluation of endmember selection techniques and performance results from ORASIS hyperspectral analysis

Kwok Yeung Tsang, John M. Grossmann, Peter J. Palmadesso, John A. Antoniades, Mark M. Baumback, Jeffrey H. Bowles, Mark Daniel, John Fisher, Daniel Haas

Author Affiliations +

Proceedings Volume 3372, Algorithms for Multispectral and Hyperspectral Imagery IV; (1998) https://doi.org/10.1117/12.312607
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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.

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Kwok Yeung Tsang, John M. Grossmann, Peter J. Palmadesso, John A. Antoniades, Mark M. Baumback, Jeffrey H. Bowles, Mark Daniel, John Fisher, and Daniel Haas "Evaluation of endmember selection techniques and performance results from ORASIS hyperspectral analysis", Proc. SPIE 3372, Algorithms for Multispectral and Hyperspectral Imagery IV, (2 July 1998); https://doi.org/10.1117/12.312607

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