Abundance quantification by independent component analysis of hyperspectral imagery for oil spill coverage calculation

Zhongzhi Han; Jianhua Wan; Jie Zhang; Hande Zhang

doi:10.1007/s00343-017-5301-8

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Abundance quantification by independent component analysis of hyperspectral imagery for oil spill coverage calculation

Updated：2023-05-16

- Abundance quantification by independent component analysis of hyperspectral imagery for oil spill coverage calculation
- Journal of Oceanology and Limnology Vol. 35, Issue 4, Pages: 978-986(2017)
- Affiliations：
  
  1. Information College Qingdao Agricultural University
  2. School of Geosciences China University of Petroleum
  3. The First Institute of Oceanography State Oceanic Administration (SOA)
  4. China Marine Surveillance Beihai Aviation Detachment
- Author bio：
- Funds：
- DOI：10.1007/s00343-017-5301-8
  CLC：
- Online First：12 August 2016，
  
  Published：2017
- Accepted：
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Han, Z., Wan, J., Zhang, J. et al. Abundance quantification by independent component analysis of hyperspectral imagery for oil spill coverage calculation., Chin. J. Ocean. Limnol. 35, 978–986 (2017). https://doi.org/10.1007/s00343-017-5301-8
DOI：

Han, Z., Wan, J., Zhang, J. et al. Abundance quantification by independent component analysis of hyperspectral imagery for oil spill coverage calculation., Chin. J. Ocean. Limnol. 35, 978–986 (2017). https://doi.org/10.1007/s00343-017-5301-8 DOI：

摘要

Abstract

The estimation of oil spill coverage is an important part of monitoring of oil spills at sea. The spatial resolution of images collected by airborne hyper-spectral remote sensing limits both the detection of oil spills and the accuracy of estimates of their size. We consider at-sea oil spills with zonal distribution in this paper and improve the traditional independent component analysis algorithm. For each independent component we added two constraint conditions: non-negativity and constant sum. We use priority weighting by higher-order statistics

and then the spectral angle match method to overcome the order nondeterminacy. By these steps

endmembers can be extracted and abundance quantified simultaneously. To examine the coverage of a real oil spill and correct our estimate

a simulation experiment and a real experiment were designed using the algorithm described above. The result indicated that

for the simulation data

the abundance estimation error is 2.52% and minimum root mean square error of the reconstructed image is 0.030 6. We estimated the oil spill rate and area based on eight hyper-spectral remote sensing images collected by an airborne survey of Shandong Changdao in 2011. The total oil spill area was 0.224 km2

and the oil spill rate was 22.89%. The method we demonstrate in this paper can be used for the automatic monitoring of oil spill coverage rates. It also allows the accurate estimation of the oil spill area.

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references

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