Discrimination of objects within polarimetric imagery using principle component and cluster analysis / Melvin Felton [and others].

Format:

Book

Government document

Contributor:

Felton, Melvin A.

U.S. Army Research Laboratory

Series:

ARL-TR (Aberdeen Proving Ground, Md.) ; 4216.

ARL-TR ; 4216

Language:

English

Subjects (All):

Polarimetry.

Infrared imaging.

Cluster analysis.

Principal components analysis.

Physical Description:

1 online resource (vi, 22 pages) : illustrations (some color).

Place of Publication:

Adelphi, MD : Army Research Laboratory, [2007]

Summary:

We apply two multivariate image analysis techniques to several sets of spatially coincident, long-wave infrared (LWIR), polarimetric images to improve target contrast and/or aid in the identification of certain object features not present in the original image set. Principal component analysis (PCA) was used to obtain new representations of the scene that highlight target features based upon the variance of the variables used in the analysis. We show a representation of the scene that maintains the same level of target-to-background contrast (when compared to the conventional thermal and degree of linear polarization images), as well as additional information content contained in the resultant PCA analysis. Cluster analysis (CA) was used to group pixels of the image that have similar values for the variables chosen. We show that this method is an effective means for separating objects of interest from complex backgrounds, as well as subdividing different features of an object.

Notes:

Title from title screen (viewed on Mar. 13, 2012).

"August 2007."

Includes bibliographical references (page 20).

Other Format:

Print version: Discrimination of objects within polarimetric imagery using principle component and cluster analysis

OCLC:

227946849