Affiliations: [a] Department of Computer Science, Texas Tech University, Lubbock, TX, USA | [b] Department of Industrial Design, Xi'an Jiaotong Liverpool University, Suzhou, Jiangsu, China | [c] Department of Industrial and Systems Engineering and RUTCOR (Rutgers Center for Operations Research), Rutgers, the State University of New Jersey, Piscataway, NJ, USA | [d] Office of the Texas State Chemist, Texas A&M, TX, USA | [e] School of Animal Biology, The UWA Institute of Agriculture, The University of Western Australia, Crawley, Perth, Western Australia, Australia
Correspondence:
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Corresponding author: Eunseog Youn, Department of Computer Science, Texas Tech University, Lubbock, TX 79409, USA. Tel.: +1 806 742 3527; Fax: +1 806 742 3519; E-mail: eun.youn@ttu.edu.
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.
