Evolution of Spatial Data Templates for Object Classification

Evolution of Spatial Data Templates for Object Classification

Neil Dunstan (University of New England, Australia) and Michael de Raadt (University of Southern Queensland, Australia)
Copyright: © 2002 |Pages: 14
DOI: 10.4018/978-1-930708-25-9.ch007
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Abstract

Sensing devices are commonly used for the detection and classification of subsurface objects, particularly for the purpose of eradicating Unexploded Ordnance (UXO) from military sites. UXO detection and classification is inherently different to pattern recognition in image processing in that signal responses for the same object will differ greatly when the object is at different depths and orientations. That is, subsurface objects span a multidimensional space with dimensions including depth, azimuth and declination. Thus the search space for identifying an instance of an object is extremely large. Our approach is to use templates of actual responses from scans of known objects to model object categories. We intend to justify a method whereby Genetic Algorithms are used to improve the template libraries with respect to their classification characteristics. This chapter describes the application, key features of the Genetic Algorithms tested and the results achieved.

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