Clutter suppression algorithm for nonimaging data

Author

Hibbeln, Brian A.

Author_Institution

national Air Intelligence Center, Washington, DC, USA

Volume

4

fYear

1999

fDate

1999

Firstpage

289

Abstract

A clutter suppression algorithm based on principle component analysis for overhead non-imaging infrared (ONIR) focal plane data is presented and its performance evaluated. The algorithm is novel in that (1) the principle components are derived from a small subset of the pixel histories and (2) probable target pixels are reevaluated in a way that substantially reduces the “lost energy” usually associated with subspace projection. The algorithm scales very favorably to larger focal planes because the principle components are derived from a small number of pixel histories. The algorithm is highly vectorizable and very well suited for parallel processing. A series of 43 actual data collections was analyzed. The raw data had mean standard deviations ranging from 4 to 4375. Clutter was reduced by over a factor of 10 using 8 principle components and over 30 for high clutter cases using 24 components

Keywords

clutter; focal planes; image processing; parallel algorithms; principal component analysis; singular value decomposition; PCA; clutter suppression algorithm; infrared focal plane data; nonimaging data processing; overhead nonimaging IR focal plane data; parallel processing; pixel histories subset; principle component analysis; Algorithm design and analysis; Biographies; Biosensors; Computer vision; Data analysis; History; Jitter; Parallel processing; Performance analysis; Taylor series;

fLanguage

English

Publisher

ieee

Conference_Titel

Aerospace Conference, 1999. Proceedings. 1999 IEEE

Conference_Location

Snowmass at Aspen, CO

Print_ISBN

0-7803-5425-7

Type

conf

DOI

10.1109/AERO.1999.792097

Filename

792097