By Topic

Improving the performance of classifiers in high-dimensional remote sensing applications: an adaptive resampling strategy for error-prone exemplars (ARESEPE)

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

1 Author(s)
C. M. Bachmann ; Remote Sensing Div., Naval Res. Lab., Washington, DC, USA

In the past, "active learning" strategies have been proposed for improving the convergence and accuracy of statistical classifiers. However, many of these approaches have large storage requirements or unnecessarily large computational burdens and, therefore, have been impractical for the large-scale databases typically found in remote sensing, especially hyperspectral applications. In this paper, we develop a practical on-line approach with only modest storage requirements. The new approach improves the convergence rate associated with the optimization of adaptive classifiers, especially in high-dimensional remote sensing data. We demonstrate the new approach using PROBE2 hyperspectral imagery and find convergence time improvements of two orders of magnitude in the optimization of land-cover classifiers.

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:41 ,  Issue: 9 )