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Optical Engineering

Neural-network-based single-frame detection of dim spot target in infrared images
Author(s): Peng Zhang; Jianxun Li
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Paper 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.

Paper Details

Date Published: 1 July 2007
PDF: 11 pages
Opt. Eng. 46(7) 076401 doi: 10.1117/1.2759236
Published in: Optical Engineering Volume 46, Issue 7
Show Author Affiliations
Peng Zhang, Shanghai Jiao Tong Univ. (China)
Jianxun Li, Shanghai Jiao Tong Univ. (China)

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