By Topic

Automated Labeling of Materials in 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)
Brian David Bue ; Department of Electrical and Computer Engineering, Rice University, Houston , TX, USA ; Erzsébet Merenyi ; Beáta Csatho

We present a technique for automatically labeling segmented hyperspectral imagery with semantically meaningful material labels. The technique compares the mean signatures of each image segment to a spectral library of known materials, and material labels are assigned to image segments according to the most similar library entry. The similarity between spectral signatures is evaluated using our recently proposed CICRd similarity measure designed specifically for hyperspectral imagery. This measure considers both the continuum-intact reflectance spectrum and its continuum-removed representation. We provide a thorough assessment of this measure by comparison to several commonly used similarity measures on a well-studied low-altitude Airborne Visible/Infrared Imaging Spectrometer image of an urban area. We evaluate our results using both information-theoretic techniques and visual validation of the resulting spectral matches.

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:48 ,  Issue: 11 )