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

Philip Wilcox, Timothy M. Horton, Eunseog Youn, Myong K. Jeong, Derrick Tate, Timothy Herrman, Christian Nansen

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)25-42
Number of pages18
JournalIntelligent Data Analysis
Volume18
Issue number1
DOIs
StatePublished - 2014

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Hyperspectral image cubes
  • animal feed quality monitoring
  • divergence
  • evolutionary search
  • hyperspectral band selection
  • recursive feature elimination
  • reflectance analysis

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