By Topic

Classification and feature extraction of hyperdimensional data using LOOC covariance estimation

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

3 Author(s)
Benediktsson, J.A. ; Eng. Res. Inst., Iceland Univ., Reykjavik, Iceland ; Ingimundarson, J.I. ; Sveinsson, J.R.

New methods for processing of multisource and hyperdimensional data are discussed both in terms of feature extraction and classification. An extension to decision boundary feature extraction (DBFE) is proposed. The extension is based on a recently developed covariance estimator, the leave one out covariance (LOOC). The extended decision boundary method is tested on a multisource remote sensing and geographic data set

Published in:

Geoscience and Remote Sensing, 1997. IGARSS '97. Remote Sensing - A Scientific Vision for Sustainable Development., 1997 IEEE International  (Volume:2 )

Date of Conference:

3-8 Aug 1997