Spectral variational mode decomposition and VMD-CR-ED model on measuring vopper and lead pollution of corn
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College of Geoscience and Surveying Engineering,China University of Mining TechnologyBeijing,College of Geoscience and Surveying Engineering,China University of Mining TechnologyBeijing,College of Geoscience and Surveying Engineering,China University of Mining TechnologyBeijing,College of Geoscience and Surveying Engineering,China University of Mining TechnologyBeijing,College of Geoscience and Surveying Engineering,China University of Mining TechnologyBeijing

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    Abstract:

    The monitoring of heavy metal pollution in crops is one important application of hyperspectral remote sensing study. Heavy metal pollution can cause spectral distortion of crop, but the crop spectra of different pollution degrees still have very high similarity in shape, so one of the key problems in monitoring its distortion is how to extract the weak difference information between similar spectra. In this study, a VMD-CR-ED measurement model was established by introducing the variational mode decomposition (VMD) theory into weak change monitoring of spectral information and combining with the continuum removal (CR) and euclidean distance (ED) methods. The model was proved to be superior in distinguishing the different pollution degrees of corn leaves by compared with SA and SCC. At the same time, the correlation between VMD-CR-ED model and eight characteristic sub-bands of corn leaf spectrum was analyzed. Finally, based on the test data, it is proved that the VMD-CR-ED measurement model is feasible and reliable in the monitoring of copper and lead pollution of crops.

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YANG Ke-Ming, SUN Tong-Tong, ZHANG Wei, CHENG Long, WANG Xiao-Feng. Spectral variational mode decomposition and VMD-CR-ED model on measuring vopper and lead pollution of corn[J]. Journal of Infrared and Millimeter Waves,2018,37(1):80~86

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History
  • Received:June 26,2017
  • Revised:September 05,2017
  • Adopted:September 07,2017
  • Online: March 19,2018
  • Published: