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17 January 2002 Evolving a spatio-spectral network on reconfigurable computers for multispectral feature identification
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Abstract
Feature identification attempts to find algorithms that can consistently separate a feature of interest from the background in the presence of noise and uncertain conditions. This paper describes the development of a high-throughput, reconfigurable computer based, feature identification system known as POOKA. POOKA is based on a novel spatio-spectral network, which can be optimized with an evolutionary algorithm on a problem-by-problem basis. The reconfigurable computer provides speed up in two places: 1) in the training environment to accelerate the computationally intensive search for new feature identification algorithms, and 2) in the application of trained networks to accelerate content based search in large multi-spectral image databases. The network is applied to several broad area features relevant to scene classification. The results are compared to those found with traditional remote sensing techniques as well as an advanced software system known as GENIE. The hardware efficiency and performance gains compared to software are also reported.
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Reid B. Porter, Maya Gokhale, Neal R. Harvey, Simon J. Perkins, and Aaron Cody Young "Evolving a spatio-spectral network on reconfigurable computers for multispectral feature identification", Proc. SPIE 4480, Imaging Spectrometry VII, (17 January 2002); https://doi.org/10.1117/12.453330
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