Linear log-likelihood ratio (L3R) algorithm for spectral target detection

Brian J. Daniel; Alan P. Schaum

doi:10.1117/12.884337

20 May 2011 Linear log-likelihood ratio (L³R) algorithm for spectral target detection

Brian J. Daniel, Alan P. Schaum

Author Affiliations +

Proceedings Volume 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII; 804804 (2011) https://doi.org/10.1117/12.884337
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

The potential of a new class of detection algorithms is demonstrated on an object of practical interest. The continuum fusion (CF) [1] methodology is applied to a linear subspace model. A new algorithm results from first invoking a fusion interpretation of a conventional GLR test and then modifying it with CF methods. Usual performance is enhanced in two ways. First the Gaussian clutter model is replaced by a Laplacian distribution, which is not only more realistic in its tail behavior but, when used in a hypothesis test, also creates decision surfaces more selective than the hyperplanes associated with linear matched filters. Second, a fusion flavor is devised that generalizes the adaptive coherence estimator (ACE) [2, 3] algorithm but has more design flexibility. An IDL/ENVI user interface has been developed and will be described.

Citation Download Citation

Brian J. Daniel and Alan P. Schaum "Linear log-likelihood ratio (L³R) algorithm for spectral target detection", Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 804804 (20 May 2011); https://doi.org/10.1117/12.884337

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