Evolutionary refinement approaches for band selection of hyperspectral images with applications to automatic monitoring of animal feed quality

Authors:
Philip Wilcox;Timothy M. Horton;Eunseog Youn;Myong K. Jeong;Derrick Tate;Timothy Herrman;Christian Nansen
Affiliations:
Department of Computer Science, Texas Tech University, Lubbock, TX, USA;Department of Industrial Design, Xi'an Jiaotong Liverpool University, Suzhou, Jiangsu, China;Department of Computer Science, Texas Tech University, Lubbock, TX, USA;Department of Industrial and Systems Engineering and RUTCOR Rutgers Center for Operations Research, Rutgers, the State University of New Jersey, Piscataway, NJ, USA;Department of Industrial Design, Xi'an Jiaotong Liverpool University, Suzhou, Jiangsu, China;Office of the Texas State Chemist, Texas A&M, TX, USA;School of Animal Biology, The UWA Institute of Agriculture, The University of Western Australia, Crawley, Perth, Western Australia, Australia
Venue:
Intelligent Data Analysis - Business Analytics and Intelligent Optimization
Year:
2014

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Abstract

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.