Evolutionary refinement approaches for band selection of hyperspectral images with applications to automatic monitoring of animal feed quality
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This paper presents methods for spectral band selection in hyperspectral image (HSI) cubes based on classification of reflectance data acquired from samples of livestock feed materials and ruminant-derived bonemeal. Automated detection of ruminant-derived bonemeal in animal feed is tested as part of an on-going research into development of automated, reliable fast and cost-effective quality control systems. HSI cubes contain spectral reflectance in both spatial dimensions and spectral bands. Support vector machines are used for classification of data in various domains. Selecting a subset of the spectral bands speeds processing and increases accuracy by reducing over-fitting. We developed two methods utilizing divergence values for selecting spectral band sets, 1) evolutionary search method and 2) divergence-based recursive feature elimination approach. 2014-IOS Press.
Intelligent Data Analysis
author list (cited authors)
Wilcox, P., Horton, T. M., Youn, E., Jeong, M. K., Tate, D., Herrman, T., & Nansen, C.
complete list of authors
Wilcox, Philip||Horton, Timothy M||Youn, Eunseog||Jeong, Myong K||Tate, Derrick||Herrman, Timothy||Nansen, Christian
editor list (cited editors)
Smith-Miles, K., & Weber, R.