Paper
7 May 2007 Evaluation testbed for ATD performance prediction (ETAPP)
Author Affiliations +
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.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Scott K. Ralph, Ross Eaton, Magnús Snorrason, John Irvine, and Steve Vanstone "Evaluation testbed for ATD performance prediction (ETAPP)", Proc. SPIE 6566, Automatic Target Recognition XVII, 656611 (7 May 2007); https://doi.org/10.1117/12.719339
Lens.org Logo
CITATIONS
Cited by 3 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Target detection

Performance modeling

Image processing

Image segmentation

Automatic target recognition

Image analysis

Sensors

Back to Top