By Topic

A Case Study of Spectral Signature Detection in Multimodal and Outlier-Contaminated Scenes

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

4 Author(s)
Thompson, D.R. ; Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA, USA ; Mandrake, L. ; Green, R.O. ; Chien, S.A.

Mapping localized spectral features in complex scenes demands sensitive and robust detection algorithms. This letter investigates two aspects of large images that can harm matched filter (MF) detection performance. First, multimodal backgrounds may violate normality assumptions. Second, outlier features can trigger false detections due to large projections onto the target vector. We review two state-of-the-art methods designed to resolve these issues. The background clustering of Funk models multimodal backgrounds, and the mixture-tuned (MT) MF of Boardman and Kruse addresses outliers. We demonstrate that combining the two methods has additional performance benefits. An MT cluster MF shows effective performance on simulated and airborne data sets. We demonstrate target detection scenarios that evidence multimodality, outliers, and their combination. These experiments explore the performance of the component algorithms and the practical circumstances that can favor a combined approach.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:10 ,  Issue: 5 )