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Specific Material Detection with Similar Colors using Feature Selection and Band Ratio in Hyperspectral Image

초분광 영상 특징선택과 밴드비 기법을 이용한 유사색상의 특이재질 검출기법

  • Shim, Min-Sheob (Department of Electronic Engineering, LED-IT Fusion Technology Research Center, Yeungnam University) ;
  • Kim, Sungho (Department of Electronic Engineering, LED-IT Fusion Technology Research Center, Yeungnam University)
  • 심민섭 (영남대학교 전자공학과) ;
  • 김성호 (영남대학교 전자공학과)
  • Received : 2013.08.20
  • Accepted : 2013.10.04
  • Published : 2013.12.01

Abstract

Hyperspectral cameras acquire reflectance values at many different wavelength bands. Dimensions tend to increase because spectral information is stored in each pixel. Several attempts have been made to reduce dimensional problems such as the feature selection using Adaboost and dimension reduction using the Simulated Annealing technique. We propose a novel material detection method that consists of four steps: feature band selection, feature extraction, SVM (Support Vector Machine) learning, and target and specific region detection. It is a combination of the band ratio method and Simulated Annealing algorithm based on detection rate. The experimental results validate the effectiveness of the proposed feature selection and band ratio method.

Keywords

References

  1. A. F. Goetz, "Three decades of hyperspectral remote sensing of the Earth: A personal view," Remote Sensing of Environment, vol. 113, pp. 5-16, 2009. https://doi.org/10.1016/j.rse.2007.12.014
  2. M. A. Karaska, R. L. Huguenin, J. L. Beacham, M. H. Wang, J. R. Jensen, and R. S. Kaufmann, "AVIRIS measurements of chlorophyll, suspended minerals, dissolved organic carbon, and turbidity in the neuse river, north carolina," Photogrammetric Engineering and Remote Sensing, vol. 70, no. 1, pp. 125-134, 2004. https://doi.org/10.14358/PERS.70.1.125
  3. J. Solomon and B. Rock, "Imaging spectrometry for earth remote sensing," Science, vol. 228, pp. 1147-1153, 1985. https://doi.org/10.1126/science.228.4704.1147
  4. S M. Schweizer and J. M. Moura, "Efficient detection in hyperspectral imagery," IEEE Transactions on Image Processing, vol. 10, no. 4, pp. 584-597, 2001. https://doi.org/10.1109/83.913593
  5. F. V. D. Meer and S. M. D. Jong, Imaging Spectrometry: Basic Principles and Prospective Applications, Springer, vol. 4, 2001.
  6. C. Bassani, R. M. Cavalli, F. Cavalcante, V. Cuomo, A. Palombo, S. Pascucci, and S. Pignatti, "Deterioration status of asbestoscement roofing sheets assessed by analyzing hyperspectral data," Remote Sensing of Environment, vol. 109, no. 3, pp. 361-378, 2007. https://doi.org/10.1016/j.rse.2007.01.014
  7. B. Park, S. C. Yoon, W. R. Windham, K. C. Lawrence, M. S. Kim, and K. Chao, "Line-scan hyperspectral imaging for realtime in-line poultry fecal detection," Sensing and Instrumentation for Food Quality and Safety, vol. 5, no. 1, pp. 25-32, 2011. https://doi.org/10.1007/s11694-011-9107-7
  8. T. Vo-Dinh, "A hyperspectral imaging system for in vivo optical diagnostics," IEEE Engineering in Medicine and Biology Magazine: the Quarterly Magazine of the Engineering in Medicine & Biology Society, vol. 23, no. 5, pp. 40-49, 2003.
  9. M. S. Alam, M. N. Islam, A. Bal, and M. A. Karim, "Hyperspectral target detection using Gaussian filter and postprocessing," Optics and Lasers in Engineering, vol. 46, no. 11, pp. 817-822, 2008. https://doi.org/10.1016/j.optlaseng.2008.05.019
  10. C. I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Springer, 2003.
  11. J. R. Jensen, Introductory Digital Image Processing: a Remote Sensing Perspective, Prentice-Hall Inc., no. Ed. 2, 1996.
  12. S. Kawaguchi and R. Nishii, "Hyperspectral image classification by bootstrap AdaBoost with random decision stumps," IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 11, pp. 3845-3851, 2007. https://doi.org/10.1109/TGRS.2007.903708
  13. Y. L. Chang, "A simulated annealing feature extraction approach for hyperspectral images," Future Generation Computer Systems, vol. 27, no. 4, pp. 419-426, 2011. https://doi.org/10.1016/j.future.2010.08.008
  14. A. A. Green, M. Berman, P. Switzer, and M. D. Craig, "A transformation for ordering multispectral data in terms of image quality with implications for noise removal," IEEE Transactions on Geoscience and Remote Sensing, vol. 26, no. 1, pp. 65-74, 1988. https://doi.org/10.1109/36.3001
  15. Z. Fu, T. Caelli, N. Liu, and A. Robles-Kelly, "Boosted band ratio feature selection for hyperspectral image classification," ICPR 2006. 18th International Conference on IEEE., vol. 1, pp. 1059-1062, 2006.
  16. S. Nakariyakul and D. Casasent, "Hyperspectral ratio feature selection: agricultural product inspection example," Proc. of SPIE, vol. 5587, pp. 133-143, 2004.
  17. S. Maji, A. C. Berg, and J. Malik, "Classification using intersection kernel support vector machines is efficient," Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE, 2008, pp. 1-8, 2008.
  18. Y. B. Joo and K. M. Huh, "Robust defect size measuring method for an automated vision inspection system," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 19, no. 11, pp. 974-978, 2013. https://doi.org/10.5302/J.ICROS.2013.13.9031
  19. S. J. Lee and S. W. Kim, "Classifying scratch defects on billets using image processing and SVM," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 19, no. 3, pp. 256-261, 2013. https://doi.org/10.5302/J.ICROS.2013.12.1849