Cart (Loading....) | Create Account
Close category search window
 

Hyperspectral Data Processing in a High Performance Computing Environment: A Parallel Best Band Selection Algorithm

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
$31 $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

2 Author(s)
Robila, S.A. ; Dept. of Comput. Sci., Montclair State Univ., Montclair, NJ, USA ; Busardo, G.

Hyper spectral data are characterized by a richness of information unique among various visual representations of a scene by representing the information in a collection of grayscale images with each image corresponding to a narrow interval in the electromagnetic spectrum. Such detail allows for precise identification of materials in the scene and promises to support advances in imaging beyond the visible range. However, hyper spectral data are considerably large and cumbersome to process and efficient computing solutions based on high performance computing are needed. In this paper we first provide an overview of hyper spectral data and the current state of the art in the use of HPC for its processing. Next we discuss the concept of best band selection, a fundamental feature extraction problem in hyper spectral imagery that, besides exhaustive search has only non optimal solutions. We provide an elegant algorithm that performs an exhaustive search for the solution using a distributed, multicore environment and MPI in order to show how using such a solution provides significant improvement over traditional sequential platforms. Additional experiments on the robustness of the algorithm in terms of data and job sizes are also provided.

Published in:

Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on

Date of Conference:

16-20 May 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.