Evaluation Of Endmember Selection Techniques And Performance Results From Orasis Hyperspectral Analysis

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.