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Proceedings Paper

Automatic target recognition using neural networks
Author(s): Steven K. Rogers; John M. Colombi; Curtis E. Martin; James C. Gainey; Kenneth H. Fielding; Thomas J. Burns; Dennis W. Ruck; Matthew Kabrisky; Mark E. Oxley
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Paper Abstract

Many applications reported in artificial neural networks are associated with military problems. This paper reviews concepts associated with the processing of military data to find and recognize targets -- automatic target recognition (ATR). A general-purpose automatic target recognition system does not exist. The work presented here is demonstrated on military data, but it can only be considered proof of principle until systems are fielded and proven `under- fire.' ATR data can be in the form of nonimaging one-dimensional sensor returns, such as ultra-high range-resolution radar returns (UHRR) for air-to-air automatic target recognition and vibration signatures from a laser radar for recognition of ground targets. The ATR data can be two-dimensional images. The most common ATR images are infrared, but current systems must also deal with synthetic aperture radar (SAR) images. Finally, the data can be three-dimensional, such as sequences of multiple exposures taken over time from a nonstationary world. Targets move, as do sensors, and that movement can be exploited by the ATR. Hyperspectral data, which are views of the same piece of the world looking at different spectral bands, is another example of multiple image data; the third dimension is now wavelength and not time. ATR system design usually consists of four stages. The first stage is to select the sensor or sensors to produce the target measurements. The next stage is the preprocessing of the data and the location of regions of interest within the data (segmentation). The human retina is a ruthless preprocessor. Physiologically motivated preprocessing and segmentation is demonstrated along with supervised and unsupervised artificial neural segmentation techniques. The third design step is feature extraction and selection: the extraction of a set of numbers which characterize regions of the data. The last step is the processing of the features for decision making (classification). The area of classification is where most ATR related neural network research has been accomplished. The relation of neural classifiers to Bayesian techniques is emphasized along with the more recent use of feature sequences to enhance classification.

Paper Details

Date Published: 6 April 1995
PDF: 15 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205140
Show Author Affiliations
Steven K. Rogers, Air Force Institute of Technology (United States)
John M. Colombi, Air Force Institute of Technology (United States)
Curtis E. Martin, Air Force Institute of Technology (United States)
James C. Gainey, Air Force Institute of Technology (United States)
Kenneth H. Fielding, Air Force Institute of Technology (United States)
Thomas J. Burns, Air Force Institute of Technology (United States)
Dennis W. Ruck, Air Force Institute of Technology (United States)
Matthew Kabrisky, Air Force Institute of Technology (United States)
Mark E. Oxley, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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