Statistical methods for analysis of hyperspectral anomaly detectors / Dalton Rosario.

Format:

Book

Government document

Author/Creator:

Rosario, Dalton.

Contributor:

U.S. Army Research Laboratory

Series:

ARL-TR (Aberdeen Proving Ground, Md.) ; 4266.

ARL-TR ; 4266

Language:

English

Subjects (All):

Optical detectors.

Atomic spectroscopy--Statistical methods.

Atomic spectroscopy.

Physical Description:

1 online resource (viii, 86 pages)

Place of Publication:

Adelphi, MD : Army Research Laboratory, [2007]

Summary:

Most hyperspectral (HS) anomaly detectors in the literature have been evaluated using a few HS imagery sets to estimate the well-known ROC curve. Although this evaluation approach can be helpful in assessing detectors rates of correct detection and false alarm on a limited dataset, it does not shed lights on reasons for these detectors strengths and weaknesses using a significantly larger sample size. This paper discusses a more rigorous approach to testing and comparing HS anomaly detectors, and it is intended to serve as a guide for such a task. Using randomly generated samples, the approach introduces hypothesis tests for two different kinds of data: (i) idealized homogeneous samples and (ii) idealized heterogeneous samples, where model parameters can vary the difficulty level of these tests. In (i), a simulation experiment is devised to address a more generalized concern the expected degradation of correct detection as a function of increasing noise on a given alternative hypothesis. In (ii), fundamental features of a spectral sample (magnitude and shape) are modeled separately so that strengths and weaknesses of competing detectors can be independently assessed for each feature. Additionally, detectors ability to suppress transition of regions in the imagery is assessed in (ii).

Notes:

Title from title screen (viewed Apr 23, 2009).

"September 2007."

Includes bibliographical references (page 71).

Electronic reproduction. Ft. Belvoir, Va. : Defense Technical Information Center, 2007.

OCLC:

318685645

Access Restriction:

Approved for public release.