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Proceedings Paper

Learning target masks in infrared linescan imagery
Author(s): Thomas Fechner; Oliver Rockinger; Axel Vogler; Peter Knappe
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Paper Abstract

In this paper we propose a neural network based method for the automatic detection of ground targets in airborne infrared linescan imagery. Instead of using a dedicated feature extraction stage followed by a classification procedure, we propose the following three step scheme: In the first step of the recognition process, the input image is decomposed into its pyramid representation, thus obtaining a multiresolution signal representation. At the lowest three levels of the Laplacian pyramid a neural network filter of moderate size is trained to indicate the target location. The last step consists of a fusion process of the several neural network filters to obtain the final result. To perform this fusion we use a belief network to combine the various filter outputs in a statistical meaningful way. In addition, the belief network allows the integration of further knowledge about the image domain. By applying this multiresolution recognition scheme, we obtain a nearly scale- and rotational invariant target recognition with a significantly decreased false alarm rate compared with a single resolution target recognition scheme.

Paper Details

Date Published: 4 April 1997
PDF: 10 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271493
Show Author Affiliations
Thomas Fechner, Daimler-Benz AG (Germany)
Oliver Rockinger, Daimler-Benz AG (Germany)
Axel Vogler, Daimler-Benz AG (Germany)
Peter Knappe, Daimler-Benz Aerospace (Germany)


Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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