Share Email Print
cover

Proceedings Paper

Results of GLMM-based target detection on the RIT data set
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The authors have recently introduced the Generalized Linear Mixing Model (GLMM), which extends the traditional Linear Mixing Model by generalizing the concept of an endmember vector to an endmember subspace. This generalization allows us to model the spectral variability that is present in a given class. The model also naturally includes the use of 'target spaces', which have been previously developed to model the variability of at-sensor radiance for a given library spectrum due to atmospheric and illumination uncertainty. In this paper, we apply the GLMM / target space approach to detecting targets in the recently released RIT test data set. In particular, we give a brief description of the underlying model, and then present our results of applying this model to the RIT data set.

Paper Details

Date Published: 13 May 2010
PDF: 11 pages
Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769523 (13 May 2010); doi: 10.1117/12.850242
Show Author Affiliations
David Gillis, U.S. Naval Research Lab. (United States)
Emmett Ientilucci, Rochester Institute of Technology (United States)
Jeffrey Bowles, U.S. Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 7695:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

© SPIE. Terms of Use
Back to Top