Title :
Ellipsoid-simplex hybrid for hyperspectral anomaly detection
Author_Institution :
Space & Remote Sensing Sci., Los Alamos Nat. Lab.; Los Alamos, Los Alamos, NM, USA
Abstract :
The problem of anomaly detection in hyperspectral imagery is expressed in terms of a minimal volume set in a high-dimensional space that encloses the bulk of the data samples. The venerable RX algorithm employs an ellipsoid for this volume, but endmember methods can be used to create a simplex volume. This paper considers a hybrid ellipsoid-simplex volume and characterizes its performance on hyperspectral imagery by computing a plot of volume versus false alarm rate. This plot provides a generic measure of quality (smaller volumes are better) without requiring the identification of specific anomalies in the data.
Keywords :
image processing; signal detection; ellipsoid simplex hybrid; high dimensional space; hyperspectral anomaly detection; hyperspectral imagery; minimal volume set; Computational modeling; Detectors; Ellipsoids; Hyperspectral imaging; Laboratories; Shape; anomaly detection; ellipsoid; endmember; false alarm rate; hyperspectral imagery; simplex; volume;
Conference_Titel :
Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011 3rd Workshop on
Conference_Location :
Lisbon
Print_ISBN :
978-1-4577-2202-8
DOI :
10.1109/WHISPERS.2011.6080893