By Topic

Parallel Implementation of Target and Anomaly Detection Algorithms for Hyperspectral Imagery

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Abel Paz ; Department of Technology of Computers and Communications, University of Extremadura, Avda. de la Universidad s/n, E-10071 Cáceres, Spain ; Antonio Plaza ; Soraya Blazquez

This paper develops several parallel algorithms for target detection in hyperspectral imagery, considered to be a crucial goal in many remote sensing applications. In order to illustrate parallel performance of the proposed parallel algorithms, we consider a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results, collected by the AVIRIS sensor over the World Trade Center, just five days after the terrorist attacks, indicate that commodity cluster computers can be used as a viable tool to increase computational performance of hyperspectral target detection applications.

Published in:

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium  (Volume:2 )

Date of Conference:

7-11 July 2008