Automated threshold determination for a family of matched subspace filters for target detection in hyperspectral images

Lewis Reynolds; Woody Kober

doi:10.1117/12.601593

19 May 2005 Automated threshold determination for a family of matched subspace filters for target detection in hyperspectral images

Lewis Reynolds, Woody Kober

Author Affiliations +

Proceedings Volume 5807, Automatic Target Recognition XV; (2005) https://doi.org/10.1117/12.601593
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

The detection of subpixel targets in hyperspectral images is complicated by interference arising from other background materials. This paper describes three target detection algorithms implemented in Data Fusion Corporation's HYPERTOOLS, a suite of hyperspectral image analysis tools. The matched subspace filter (MSF) is a generalized likelihood ratio test designed to detect target signatures while suppressing known interference signatures in a hyperspectral image. The fill-factor matched subspace filter (FFMSF) and the mixture-modeled matched subspace filter (MMMSF) extend the MSF by fusing geometrical (i.e., material abundance) and statistical (i.e., an assessment of the applicability of a linear replacement mixture model) information with the MSF output. The MSF, FFMSF, and MMMSF require one, two, and three thresholds, respectively. Automated means of determining these thresholds are proposed and justified. The MSF is further designed to allow the processing of multirank target and interference spectral matrices. As more information about a target or targets is included in the MSF, the detection performance of the MSF is expected to improve. If the target and/or interference matrices are singular or nearly singular, however, the performance of the MSF may instead be degraded. Singular value decomposition (SVD) may be employed to prepare spectral data matrices for optimal performance of the MSF. Although the use of singular value decomposition for preprocessing data matrices is well-known in signal processing, the determination of thresholds for the selection of left-singular vectors spanning the data space remains more of “an art.” An automated method for determining the number of useful left-singular vectors is proposed based on an interpretation of the singular values and on the analysis of the dimensions of the measurement space.

Citation Download Citation

Lewis Reynolds and Woody Kober "Automated threshold determination for a family of matched subspace filters for target detection in hyperspectral images", Proc. SPIE 5807, Automatic Target Recognition XV, (19 May 2005); https://doi.org/10.1117/12.601593

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Target detection

Hyperspectral imaging

Hyperspectral target detection

Image analysis

Matrices

Data modeling

Filtering (signal processing)

Show All Keywords

Keywords/Phrases

Search In:

Publication Years