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Details for article 4 of 8 found articles
| Title: |
Evolutionary refinement approaches for band selection of hyperspectral images with applications to automatic monitoring of animal feed quality |
| Author: |
Wilcox, Philip Horton, Timothy M. Youn, Eunseog Jeong, Myong K. Tate, Derrick Herrman, Timothy Nansen, Christian |
| Appeared in: |
Intelligent data analysis |
| Paging: | Volume 18 (2014) nr. 1 pages 25-42 |
| Year: |
2014-01-07 |
| Contents: |
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. |
| Publisher: |
IOS Press |
| Source file: |
Elektronische Wetenschappelijke Tijdschriften |
Details for article 4 of 8 found articles
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