False alarm mitigation techniques for hyperspectral target detection

M. L. Pieper; D. Manolakis; E. Truslow; T. Cooley; M. Brueggeman

doi:10.1117/12.2015906

18 May 2013 False alarm mitigation techniques for hyperspectral target detection

M. L. Pieper, D. Manolakis, E. Truslow, T. Cooley, M. Brueggeman

Proceedings Volume 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX; 874304 (2013) https://doi.org/10.1117/12.2015906
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

A challenging problem of major importance in hyperspectral imaging applications is the detection of subpixel objects of military and civilian interest. High false alarm thresholds are required to detect subpixel objects due to the large amount of surrounding background clutter. These high false alarm rates are unacceptable for military purposes, requiring the need for false alarm mitigation (FAM) techniques to weed out the objects of interest. The objective of this paper is to provide a comparison of the implementation of these FAM techniques and their inherent benefits in the whitened detection space. The widely utilized matched filter (MF) and adaptive cosine estimator (ACE) are both based on a linear mixing model (LMM) between a background and object class. The matched filter approximates the object abundance, and the ACE measures the model error. Each of these measurements provides inadequate object separation alone, but by using both the object abundance and model error, the objects can be separated from the false alarms.

Citation Download Citation

M. L. Pieper, D. Manolakis, E. Truslow, T. Cooley, and M. Brueggeman "False alarm mitigation techniques for hyperspectral target detection", Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 874304 (18 May 2013); https://doi.org/10.1117/12.2015906

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