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Proceedings Paper

Target detection utilizing neural networks and modified high-order correlation method
Author(s): Jeffrey H. Nanbara; Mahmood R. Azimi-Sadjadi
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Paper Abstract

This paper presents a new method for detection/classification of surface-laid land mines fom infrared imagery. This is a multistage process of preprocessing, feature extraction, neural network detection/classification, and path finding utilizing the modified high order correlation (MHOC) method. The preprocessing consists of remapping the image distribution such that the conspicuity of targets are enhanced and the background noise/clutter is suppressed as much as possible. The target feature extraction is accomplished by evaluating the principal component (PC) of blocks of data from the image. The benfit to this feature extraction is that the PCs are decorrelated. A recursive least square (RLS) algorithm is implemented in training of the PC extraction network that performs the feature extraction. Once the PCs are found, they are then used to train and test a three-layer back-propagation neural network to detect and classify the targets. The MHOC method is then applied to the resultant image to further reduce the false positives in the image. This method forms a sequence of cross-correlations and determines the consistency of correlations for path finding. The MHOC method can also be realized in a neural network structure. The simulation results, some of which are included, clearly show the detected mine paths with only a small number of false positives.

Paper Details

Date Published: 20 June 1995
PDF: 11 pages
Proc. SPIE 2496, Detection Technologies for Mines and Minelike Targets, (20 June 1995); doi: 10.1117/12.211365
Show Author Affiliations
Jeffrey H. Nanbara, Colorado State Univ. (United States)
Mahmood R. Azimi-Sadjadi, Colorado State Univ. (United States)


Published in SPIE Proceedings Vol. 2496:
Detection Technologies for Mines and Minelike Targets
Abinash C. Dubey; Ivan Cindrich; James M. Ralston; Kelly A. Rigano, Editor(s)

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