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Segmentation of Hyperspectral Images for the Detection of Rotten Mandarins

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 5112)

Abstract

The detection of rotten citrus in packing lines is carried out manually under ultraviolet illumination, which is dangerous for workers. Light emitted by the rotten region of the fruit due to the ultraviolet-induced fluorescence is used by the operator to detect the damages. This procedure is required because the low contrast between the damaged and sound skin under visible illumination difficult their detection. We study a set of techniques aimed to detect rottenness in citrususing visible and near infrared lighting trough an hyperspectral imaging system. Methods for selecting a proper set of wavelengths are investigated such as correlation analysis, mutual information, stepwise or genetic algorithms. The image segmentation relies on the combination of band selection techniques and pixel classification methods such as classification and regression trees and linear discriminant analysis.

Keywords

  • feature selection
  • hyperspectral imaging
  • pixel classification
  • fruit inspection

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  • DOI: 10.1007/978-3-540-69812-8_107
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Gómez-Sanchis, J., Camps-Valls, G., Moltó, E., Gómez-Chova, L., Aleixos, N., Blasco, J. (2008). Segmentation of Hyperspectral Images for the Detection of Rotten Mandarins. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_107

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  • DOI: https://doi.org/10.1007/978-3-540-69812-8_107

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69811-1

  • Online ISBN: 978-3-540-69812-8

  • eBook Packages: Computer ScienceComputer Science (R0)