By Topic

Automatic target detection using dualband infrared 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
$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)
Chan, L.A. ; US Army Res. Lab., Adelphi, MD, USA ; Der, S. ; Nasrabadi, N.M.

An automatic target detector often produces too many false alarms that could bog down the performance of a subsequent target classifier. Therefore, we need a good clutter rejector to remove as many clutterers as possible, before feeding the most likely target detections to the classifier. We investigate the benefits of using dual-band forward-looking infrared images to improve the performance of an eigen-neural based clutter rejector. With individual or combined bands as input, we use either principal component analysis or the eigenspace separation transform to perform feature extraction and dimensionality reduction. The transformed data is then fed to a properly trained multilayer perceptron that predicts the identity of the input, which is either a target or clutter. Experimental results are presented on a dataset of real dualband images

Published in:

Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on  (Volume:6 )

Date of Conference: