Neural-network-based single-frame detection of dim spot target in infrared images

Peng Zhang; Jianxun Li

doi:10.1117/1.2759236

1 July 2007 Neural-network-based single-frame detection of dim spot target in infrared images

Peng Zhang, Jianxun Li

Author Affiliations +

Optical Engineering, Vol. 46, Issue 7, 076401 (July 2007). https://doi.org/10.1117/1.2759236

Abstract

Infrared (IR) images derived from cloudy skies are always too spatially varied for tiny targets to be detected, especially for single-frame detection. Using the neural networks (NN) nonlinear regression, discrimination, and self-leaning capability, an NN-based method is proposed for tiny point target detection in single-frame IR images with high background clutter. First, the background was estimated by an improved NN-based morphologic filter, the structure element of which was optimized by a two-layer NN. Second, noise characteristics were well studied, and thus a two-level segmentation is presented to delete noises as well as to further remove remaining background components. Last, images with several potential targets were fed to a BP NN that predicted the identity of the input, which was either a target or a pseudo-target. It is these two neural networks that separate target from background and pseudo-target, respectively, with different training destinations, thus avoiding the over-training problem. Results on real data indicate that, given the false alarm probability, the detection probability by this method reaches 98.3%, which is improved by 11.02% compared to the traditional approach with fixed SE and without trained NN.

©(2007) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Peng Zhang and Jianxun Li "Neural-network-based single-frame detection of dim spot target in infrared images," Optical Engineering 46(7), 076401 (1 July 2007). https://doi.org/10.1117/1.2759236

Published: 1 July 2007

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