Hyperspectral target detection using a Bayesian likelihood ratio test

Author

Schaum, A.

Author_Institution

Naval Res. Lab., Washington, DC, USA

Volume

3

fYear

2002

fDate

2002

Abstract

A new approach to multivariate detection has been devised, which replaces the generalized likelihood ratio (GLR) with another, the Bayesian likelihood ratio (BLR). The new test is partly based on selectable prior distributions of the parameters appearing in the GLR. Through these distributions, the method facilitates the incorporation of prior knowledge generated by simple physics and experience in measurement programs. This paper explores these potentialities by applying the new formalism to the problem of matched subspace detection in hyperspectral data sets.

Keywords

Bayes methods; clutter; image recognition; multivariable systems; object detection; remote sensing; BLR formalism; GLR parameter selectable prior distributions; clutter; generalized likelihood ratio; hyperspectral data sets; hyperspectral imaging; hyperspectral remote sensing applications; hyperspectral target detection using Bayesian likelihood ratio test; matched subspace detection; measurement programs; multivariate detection; multivariate signal processing; physics/experience prior knowledge incorporation; Bayesian methods; Detectors; Hyperspectral imaging; Hyperspectral sensors; Object detection; Remote sensing; Signal processing; Space technology; Spectroscopy; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Aerospace Conference Proceedings, 2002. IEEE

Print_ISBN

0-7803-7231-X

Type

conf

DOI

10.1109/AERO.2002.1035292

Filename

1035292