By Topic

Discriminant Absorption-Feature Learning for Material Classification

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

2 Author(s)
Zhouyu Fu ; Gippsland School of Information Technology, Monash University, Churchill, Australia ; Antonio Robles-Kelly

In this paper, we develop a novel approach to object-material identification in spectral imaging by combining the use of invariant spectral absorption features and statistical machine-learning techniques. Our method hinges on the relevance of spectral absorption features for material identification and casts the problem into a pattern-recognition setting by making use of an invariant representation of the most discriminant band segments in the spectra. Thus, here, we view the identification problem as a classification task, which is effected based upon those invariant absorption segments in the spectra which are most discriminative between the materials under study. To robustly recover those bands that are most relevant to the identification process, we make use of discriminant learning. To illustrate the utility of our method for purposes of material identification, we perform experiments on both terrestrial and remotely sensed hyperspectral imaging data and compare our results to those yielded by an alternative.

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:49 ,  Issue: 5 )