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

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

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