Advertisement

Segmentation of Hyperspectral Images for the Detection of Rotten Mandarins

  • J. Gómez-Sanchis
  • G. Camps-Valls
  • E. Moltó
  • L. Gómez-Chova
  • N. Aleixos
  • J. Blasco
Part of the Lecture Notes in Computer Science book series (LNCS, 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 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blasco, J., Cubero, S., Arias, R., Gómez, J., Juste, F., Moltó., E.: Development of a computer vision system for the automatic quality grading of mandarin segments. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4478, pp. 460–466. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Blasco, J., Aleixos, N., Moltó, E.: Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of Food Engineering 81(3), 535–543 (2007)CrossRefGoogle Scholar
  3. 3.
    Blasco, J., Aleixos, N., Gómez-Sanchis, J., Moltó, E.: Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering 83(3), 384–393 (2007)CrossRefGoogle Scholar
  4. 4.
    Chang, C.I.: Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Springer, New York (2003)Google Scholar
  5. 5.
    Chen, R.K., Yang, C.M.: Estimating rice growth using ground-based hyperspectral reflectance data and simulated SPOT broad band data. Journal of Agricultural Research of China 51(4), 1–18 (2002)Google Scholar
  6. 6.
    Martínez-Sotoca, J., Plá, F.: Hyperspectral Data Selection from Mutual Information Between Image Bands. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 853–861. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Yang, C., Everitt, J.H., Bradford, J.M.: Airborne hyperspectral imagery and yield monitor data for estimating grain sorghum yield variability. Transactions of the ASAE 47(3), 915–924 (2004)zbMATHGoogle Scholar
  8. 8.
    Yao, H., Tian, L.: A genetic-algorithm-based selective principal component analysis (GA-SPCA) method for high-dimensional data feature extraction. IEEE Transactions on Geoscience and Remote Sensing 41(6), 1469–1478 (2006)Google Scholar
  9. 9.
    Steingberg, P., Colla, P.: CART. Classification and Regression Trees. Salford Systems. San Diego (1997)Google Scholar
  10. 10.
    Gómez-Chova, L., Calpe, J., Soria, E., Camps-Valls, G., Martín, J.D., Moreno, J.: CART-based feature selection of hyperspectral images for crop cover classification. In: ICIP Proceedings of the International Conference on Image Processing, vol. 3, pp. 589–592 (2003)Google Scholar
  11. 11.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. Wiley-Interscience, New York (2000)Google Scholar
  12. 12.
    Bajksy, P., Kooper, R.: Prediction accuracy of color imagery from hyperspectral imagery (last accessed January 2008), http://algdocs.ncsa.uiuc.edu/PB-20050328-2.pdf
  13. 13.
    Gómez-Sanchis, J., Moltó, E., Camps-Valls, G., Gómez-Chova, L., Aleixos, N., Blasco, J.: Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits. Journal of Food Engineering 85(2), 191–200 (2008)CrossRefGoogle Scholar
  14. 14.
    Blum, A.V., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97, 245–271 (1997)CrossRefMathSciNetzbMATHGoogle Scholar
  15. 15.
    Kohavi, R., John, G.H.: Wrappers for features subset selection. Artificial Intelligence 97, 273–324 (1997)CrossRefzbMATHGoogle Scholar
  16. 16.
    Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Boston (1989)zbMATHGoogle Scholar
  17. 17.
    Breiman, L., Friedman, J., Olshen, R., Stone, J.: Classification and regression trees. CRC Press, Boca Raton (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • J. Gómez-Sanchis
    • 1
  • G. Camps-Valls
    • 2
  • E. Moltó
    • 1
  • L. Gómez-Chova
    • 2
  • N. Aleixos
    • 3
  • J. Blasco
    • 2
  1. 1.Centro de AgroIngeniería. Instituto Valenciano de Investigaciones Agrarias (IVIA)(Valencia)Spain
  2. 2.Digital Signal Processing Group, (GPDS). Electronic Engineering DepartmentUniversity of Valencia(Valencia)Spain
  3. 3.Department of Graphics Engineering, DIG – ETSIIPolytechnic University of Valencia (UPV)ValenciaSpain

Personalised recommendations