Results of GLMM-based target detection on the RIT data set

David Gillis; Emmett Ientilucci; Jeffrey Bowles

doi:10.1117/12.850242

13 May 2010 Results of GLMM-based target detection on the RIT data set

David Gillis, Emmett Ientilucci, Jeffrey Bowles

Proceedings Volume 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI; 769523 (2010) https://doi.org/10.1117/12.850242
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

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.

Citation Download Citation

David Gillis, Emmett Ientilucci, and Jeffrey Bowles "Results of GLMM-based target detection on the RIT data set", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769523 (13 May 2010); https://doi.org/10.1117/12.850242

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available