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Composite Class Models
for SAR Recognition
Presented by: Grinnell Jones
Abstract:
This presentation focuses on a genetic algorithm based method
that automates the construction of local feature based composite
class models to capture the salient characteristics of configuration
variants of vehicle targets in Synthetic Aperture Radar (SAR)
imagery. The recognition models are based on quasi-invariant
local features: SAR scattering center locations and magnitudes.
The approach uses an efficient SAR recognition system as an
evaluation function to determine the fitness of candidate
members of a genetic population of new models and synthetically
generates composite class models. Experimental results are
given on the fitness of the composite models and the similarity
of both the original training model configurations and the
synthesized composite models to the test configurations.
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