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Integration of Spatial Information in Hyperspectral Imaging for Real Time Quality Control in an Andalusite Processing Line

  • A. Prieto
  • F. Bellas
  • F. López-Peña
  • R. J. Duro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4253)

Abstract

This paper presents an ANN hyperspectral classification system specifically developed to perform the quality control of an andalusite processing line. The main problem with these types of tasks is related with the way the ground truth is obtained, leading to labels that correspond to large areas with inhomogeneous contents. Thus, when any type of learning algorithm is used in order to train ANN based classifiers, one has to be sure that the samples presented to the networks really contain spectra that correspond to the labels. Therefore, a previous study on the size of the windows to be used by the ANNs as well as the way the information from the different pixels in these windows are combined must be carried out. The ANNs in the segmentation operator are based on Gaussian functions. The results obtained have shown that success rates, which were very poor when working with the spectral information of individual pixels, can be improved to better than 95%.

Keywords

Hyperspectral Image Average Spectrum Ground Truth Image Virtual Instrument Endmember Extraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Glackin, D.L., Peltzer, G.R.: Civil, Commercial, and International Remote Sensing Systems and Geoprocessing. American Institute of Aeronautics and Astronautics (1999)Google Scholar
  2. 2.
    Shaw, G., Manolakis, D.: Signal processing for hyperspectral image exploitation. IEEE Signal Process. Mag. 19, 12–16 (2002)CrossRefGoogle Scholar
  3. 3.
    Willoughby, C.T., Folkman, M.A., Figueroa, M.A.: Application of hyperspectral imaging spectrometer systems to industrial inspection. In: Three-Dimensional and Unconventional Imaging for Industrial Inspection and Metrology. In: Proceedings of SPIE, vol. 2599, pp. 264–272 (January 1996)Google Scholar
  4. 4.
    McCracken, W.H., Kendall, T.: Andalusite review 1995: Industrial Minerals, vol. 346, pp. 53–54 (1996)Google Scholar
  5. 5.
    Landgrebe, D.: Indian Pines AVIRIS Hyperspectral Reflectance Data: 92av3c (1992), Available at: http://makalu.jpl.nasa.gov/
  6. 6.
    Guatieri, J.A., Bechdol, M., Chettri, S., Robinson, J.W., Garegnani, J., Vermeulen, A., Antonille, S.: From Spectra to Cassification. In: Extended paper of the presentation at 1st International Symposium on Hyperspectral Analysis, Caceres, Spain, September 20-22 (2000)Google Scholar
  7. 7.
    Prieto, A.G., Bellas, F., Duro, R.J., Lopez-Peña, F.: A Comparison of Gaussian Based ANNs for the Classification of Multidimensional Hyperspectral Signals. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 829–836. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Crespo, J.L., Duro, R.J., López Peña, F.: Gaussian Synapse ANNs in Multi and Hyperspectral Image Data Analysis. IEEE Transactions on Instrumentation and Measurement 52(3), 724–732 (2003)CrossRefGoogle Scholar
  9. 9.
    López-Peña, F., Duro, R.J.: A Hyperspectral Based Multisensor System for Marine Oil Spill Detection, Analysis and Tracking. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS, vol. 3213, pp. 669–676. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • A. Prieto
    • 1
  • F. Bellas
    • 1
  • F. López-Peña
    • 1
  • R. J. Duro
    • 1
  1. 1.Integrated Group for Engineering ResearchUniversidade da Coruña 

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