ANVIL neural network program for three-dimensional automatic target recognition

William Thoet; Timothy G. Rainey; Dean W. Brettle; Lee A. Slutz; Fred Weingard

doi:10.1117/12.60008

1 December 1992 ANVIL neural network program for three-dimensional automatic target recognition

William Thoet, Timothy G. Rainey, Dean W. Brettle, Lee A. Slutz, Fred Weingard

Author Affiliations +

Optical Engineering, Vol. 31, Issue 12, (December 1992). https://doi.org/10.1117/12.60008

Abstract

The focus of the artificial neural vision learning (ANVIL) program is to apply neural network technologies to the air-to-surface 3-D automatic target recognition (ATR) problem. The 3-D multiple object detection and location system (MODALS) neural network was developed under the ANVIL program to simultaneously detect, locate, segment, and identify multiple targets. The performance results show a very high dentification accuracy, a high detection rate, and a low false alarm rate, even for areas with high clutter and shadowing. The results are shown as detection/false alarm curves and identification/false alarm curves. In addition, positional detection accuracy is shown for various scale sizes. To provide data for the program, visible terrain board imagery was collected under a variety of background and lighting conditions. Tests were made on more than 500 targets of five types and two classes. These targets varied in scale by up to − 25%, varied in azimuth by up to 120 deg, and varied in elevation by up to 10 deg. The performance results are shown for targets with resolution ranging from 9 to 700 pixels on target.

Citation Download Citation

William Thoet, Timothy G. Rainey, Dean W. Brettle, Lee A. Slutz, and Fred Weingard "ANVIL neural network program for three-dimensional automatic target recognition," Optical Engineering 31(12), (1 December 1992). https://doi.org/10.1117/12.60008

Published: 1 December 1992

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