Using Multiple Robust Parameter Design Techniques to Improve Hyperspectral Anomaly Detection Algorithm Performance (Paperback) (ISBN-13: 9781288229970)

Detecting and identifying objects of interest is the goal of all remote sensing. New advances, specifically in hyperspectral imaging technology have provided the analyst with immense amounts of data requiring evaluation. Several filtering techniques or anomaly detection algorithms have been proposed. However, most new algorithms are insuciently verified to be robust to the broad range of hyperspectral data being made available. One such algorithm, AutoGAD, is tested here via two separate robust parameter design techniques to determine optimal parameters for consistent performance on a range of data with large attribute variances. Additionally, the results of the two techniques are compared for overall e ectiveness. The results of the test as well as optimal parameters for AutoGAD are presented and future research e orts proposed.

Author: Matthew T. Davis
ISBN-10: 1288229976
ISBN-13: 9781288229970
Publisher: Biblioscholar
Language: English
Published: 10/29/2012
Pages: 160
Format: Paperback
Weight: 0.66lbs
Size: 9.69h x 7.44w x 0.34d