Maintaining CFAR operation in hyperspectral target detection using extreme value distributions

D. Manolakis; D. Zhang; M. Rossacci; R. Lockwood; T. Cooley; J. Jacobson

doi:10.1117/12.718373

10 May 2007 Maintaining CFAR operation in hyperspectral target detection using extreme value distributions

D. Manolakis, D. Zhang, M. Rossacci, R. Lockwood, T. Cooley, J. Jacobson

Proceedings Volume 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII; 65651W (2007) https://doi.org/10.1117/12.718373
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

One of the primary motivations for statistical LWIR background characterization studies is to support the design, evaluation, and implementation of algorithms for the detection of various types of ground targets. Typically, detection is accomplished by comparing the detection statistic for each test pixel to a threshold. If the statistic exceeds the threshold, a potential target is declared. The threshold is usually selected to achieve a given probability of false alarm. In addition, in surveillance applications, it is almost always required that the system will maintain a constant false alarm rate (CFAR) as the background distribution changes. This objective is usually accomplished by adaptively estimating the background statistics and adjusting the threshold accordingly. In this paper we propose and study CFAR threshold selection techniques, based on tail extrapolation, for a detector operating on hyperspectral imaging data. The basic idea is to obtain reliable estimates of the background statistics at low false alarm rates, and then extend these estimates beyond the range supported by the data to predict the thresholds at lower false alarm rates. The proposed techniques are based on the assumption that the distribution in the tail region of the detection statistics is accurately characterized by a member of the extreme value distributions. We focus on the generalized Pareto distribution. The evaluation of the proposed techniques will be done with both simulated data and real hyperspectral imaging data collected using the Army Night Vision Laboratory COMPASS sensor.

Citation Download Citation

D. Manolakis, D. Zhang, M. Rossacci, R. Lockwood, T. Cooley, and J. Jacobson "Maintaining CFAR operation in hyperspectral target detection using extreme value distributions", Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 65651W (10 May 2007); https://doi.org/10.1117/12.718373

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