Share Email Print
cover

Proceedings Paper

Evaluation testbed for ATD performance prediction (ETAPP)
Author(s): Scott K. Ralph; Ross Eaton; Magnús Snorrason; John Irvine; Steve Vanstone
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Automatic target detection (ATD) systems process imagery to detect and locate targets in imagery in support of a variety of military missions. Accurate prediction of ATD performance would assist in system design and trade studies, collection management, and mission planning. A need exists for ATD performance prediction based exclusively on information available from the imagery and its associated metadata. We present a predictor based on image measures quantifying the intrinsic ATD difficulty on an image. The modeling effort consists of two phases: a learning phase, where image measures are computed for a set of test images, the ATD performance is measured, and a prediction model is developed; and a second phase to test and validate performance prediction. The learning phase produces a mapping, valid across various ATR algorithms, which is even applicable when no image truth is available (e.g., when evaluating denied area imagery). The testbed has plug-in capability to allow rapid evaluation of new ATR algorithms. The image measures employed in the model include: statistics derived from a constant false alarm rate (CFAR) processor, the Power Spectrum Signature, and others. We present performance predictors for two trained ATD classifiers, one constructed using using GENIE ProTM, a tool developed at Los Alamos National Laboratory, and the other eCognitionTM, developed by Definiens (http://www.definiens.com/products). We present analyses of the two performance predictions, and compare the underlying prediction models. The paper concludes with a discussion of future research.

Paper Details

Date Published: 7 May 2007
PDF: 11 pages
Proc. SPIE 6566, Automatic Target Recognition XVII, 656611 (7 May 2007); doi: 10.1117/12.719339
Show Author Affiliations
Scott K. Ralph, Charles River Analytics (United States)
Ross Eaton, Charles River Analytics (United States)
Magnús Snorrason, Charles River Analytics (United States)
John Irvine, SAIC (United States)
Steve Vanstone, AMRDEC (United States)


Published in SPIE Proceedings Vol. 6566:
Automatic Target Recognition XVII
Firooz A. Sadjadi, Editor(s)

© SPIE. Terms of Use
Back to Top