Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
권호 표지
DOI QR코드

DOI QR Code

Detection of Toluene Hazardous and Noxious Substances (HNS) Based on Hyperspectral Remote Sensing

초분광 원격탐사 기반 위험·유해물질 톨루엔 탐지

  • Park, Jae-Jin (Department of Earth Science Education, Seoul National University) ;
  • Park, Kyung-Ae (Department of Earth Science Education, Seoul National University) ;
  • Foucher, Pierre-Yves (Theoretical and Applied Optics Department, ONERA) ;
  • Kim, Tae-Sung (Maritime Safety and Environmental Research Division, Korea Research Institute of Ships and Ocean Engineering) ;
  • Lee, Moonjin (Maritime Safety and Environmental Research Division, Korea Research Institute of Ships and Ocean Engineering)
  • 박재진 (서울대학교 지구과학교육과) ;
  • 박경애 (서울대학교 지구과학교육과) ;
  • ;
  • 김태성 (선박해양플랜트연구소 해양안전환경연구본부) ;
  • 이문진 (선박해양플랜트연구소 해양안전환경연구본부)
  • Received : 2021.12.15
  • Accepted : 2021.12.24
  • Published : 2021.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

The increased transport of marine hazardous and noxious substances (HNS) has resulted in frequent HNS spill accidents domestically and internationally. There are about 6,000 species of HNS internationally, and most of them have toxic properties. When an accidental HNS spill occurs, it can destroys the marine ecosystem and can damage life and property due to explosion and fire. Constructing a spectral library of HNS according to wavelength and developing a detection algorithm would help prepare for accidents. In this study, a ground HNS spill experiment was conducted in France. The toluene spectrum was determined through hyperspectral sensor measurements. HNS present in the hyperspectral images were detected by applying the spectral mixture algorithm. Preprocessing principal component analysis (PCA) removed noise and performed dimensional compression. The endmember spectra of toluene and seawater were extracted through the N-FINDR technique. By calculating the abundance fraction of toluene and seawater based on the spectrum, the detection accuracy of HNS in all pixels was presented as a probability. The probability was compared with radiance images at a wavelength of 418.15 nm to select abundance fractions with maximum detection accuracy. The accuracy exceeded 99% at a ratio of approximately 42%. Response to marine spills of HNS are presently impeded by the restricted access to the site because of high risk of exposure to toxic compounds. The present experimental and detection results could help estimate the area of contamination with HNS based on hyperspectral remote sensing.

국내외 해상 위험·유해물질(HNS, Hazardous and Noxious Substances) 물동량 증가와 함께 HNS 유출 사고가 빈번히 발생하고 있다. HNS는 전 세계적으로 약 6,000여 종으로 대부분 유독한 성질을 가지므로 이러한 유출 사고 발생은 해양 생태계 파괴를 비롯하여 폭발 및 화재 등으로 인한 인명 및 재산피해를 유발한다. 따라서 해상 HNS 유출 사고를 대비하여 파장에 따른 HNS 분광 라이브러리 구축 및 탐지 알고리즘을 개발해야 한다. 본 연구에서는 프랑스 현지에서 지상 HNS 유출 실험을 진행하였다. 초분광센서 관측을 통해 파장에 따른 톨루엔 라이브러리 스펙트럼을 구축하였으며, 분광혼합 알고리즘을 활용하여 초분광 HNS를 탐지하였다. 전처리 과정으로 주성분 분석을 적용하여 노이즈 제거 및 차원 압축을 수행하였으며, N-FINDR 기법을 통해 영상을 대표하는 톨루엔과 해수의 엔드멤버 스펙트럼을 추출하였다. 스펙트럼 기반의 톨루엔 및 해수의 점유비율을 계산함으로써 모든 픽셀의 HNS 탐지 정확도를 확률로 제시하였다. 최대 탐지 정확도를 가지는 점유비율 선정을 위해 418.15 nm 파장의 복사도 영상과 비교하였으며, 그 결과 약 42%의 비율에서 99% 이상의 정확도를 나타내었다. 해상 HNS 유출은 높은 위험성으로 인해 사람이 쉽게 접근할 수 없는 한계를 지닌다. 본 HNS 실험과정 및 탐지 결과는 초분광 원격탐사에 기반한 HNS 오염 해역 추정에 도움이 될 것이다.

Keywords

Acknowledgement

이 논문은 2021년 해양수산부 재원으로 한국해양과학기술진흥원의 지원을 받아 수행된 연구(위험유해물질(HNS)사고 관리기술 개발)이다.

References

  1. Angelliaume, S., Minchew, B., Chataing, S., Martineau, P., and Miegebielle, V., 2017, Multifrequency radar imagery and characterization of hazardous and noxious substances at sea. IEEE Transactions on Geoscience and Remote Sensing, 55(5), 3051-3066. https://doi.org/10.1109/TGRS.2017.2661325
  2. Brando, V. E. and Dekker, A. G., 2003, Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1378-1387. https://doi.org/10.1109/TGRS.2003.812907
  3. Chang, C. I., Wu, C. C., and Tsai, C. T., 2010, Random N-finder (N-FINDR) endmember extraction algorithms for hyperspectral imagery. IEEE Transactions on Image Processing, 20(3), 641-656. https://doi.org/10.1109/TIP.2010.2071310
  4. Chen, J., Di, Z., Shi, J., Shu, Y., Wan, Z., Song, L., and Zhang, W., 2020, Marine oil spill pollution causes and governance: A case study of Sanchi tanker collision and explosion. Journal of Cleaner Production, 273, 122978. https://doi.org/10.1016/j.jclepro.2020.122978
  5. Griffin, M. K. and Burke, H. H. K., 2003, Compensation of hyperspectral data for atmospheric effects. Lincoln Laboratory Journal, 14(1), 29-54.
  6. Han, D. G., Seo, H. C., Choi, J. W., and Lee, M., 2018, Experiment and Simulation of Acoustic Detection for the Substitute for Sunken Hazardous and Noxious Substances Using the High Frequency Active Sonar. Journal of the Korean Society of Marine Environment and Safety, 24(4), 459-466. https://doi.org/10.7837/kosomes.2018.24.4.459
  7. Harold, P. D., De Souza, A. S., Louchart, P., Russell, D., and Brunt, H., 2014, Development of a risk-based prioritisation methodology to inform public health emergency planning and preparedness in case of accidental spill at sea of hazardous and noxious substances (HNS). Environment International, 72, 157-163. https://doi.org/10.1016/j.envint.2014.05.012
  8. Heinz, D. C., 2001, Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 39(3), 529-545. https://doi.org/10.1109/36.911111
  9. Hirano, A., Madden, M., and Welch, R., 2003, Hyperspectral image data for mapping wetland vegetation. Wetlands, 23(2), 436-448. https://doi.org/10.1672/18-20
  10. Hwang, H. J., 2020, Analysis of Performance Requirements of Mechanical System for Recovery of Deposited Hazardous and Noxious Substances from Seabed around Seaport. Journal of the Korean Society of Marine Environment and Safety, 26(6), 681-688. https://doi.org/10.7837/kosomes.2020.26.6.681
  11. Jeong, M. G., Lee, M., and Lee, E. B., 2017, A Study on the Visualization of HNS Hazard Levels to Prevent Accidents at Sea in Real-Time. Journal of the Korean Society of Marine Environment & Safety, 23(3), 242-249. https://doi.org/10.7837/kosomes.2017.23.3.242
  12. Kim, K. S., 2021, Study on Improvements to Domestic Marine HNS Training Curricula through a Case Analysis of Marine Chemical Incidents. Journal of the Korean Society of Marine Environment & Safety, 27(1), 97-112. https://doi.org/10.7837/kosomes.2021.27.1.097
  13. Kim, T. S., Park, K., Lee, M. S., Park, J. J., Hong, S., Kim, K. L., and Chang, E., 2013, Application of bimodal histogram method to oil spill detection from a satellite synthetic aperture radar image. Korean Journal of Remote Sensing, 29(6), 645-655. https://doi.org/10.7780/KJRS.2013.29.6.7
  14. Kim, T. S., Park, K. A., Li, X., Lee, M., Hong, S., Lyu, S. J., and Nam, S., 2015, Detection of the Hebei Spirit oil spill on SAR imagery and its temporal evolution in a coastal region of the Yellow Sea. Advances in Space Research, 56(6), 1079-1093. https://doi.org/10.1016/j.asr.2015.05.040
  15. Kim, Y. R., Lee, M., Jung, J. Y., Kim, T. W., and Kim, D., 2019, Initial environmental risk assessment of hazardous and noxious substances (HNS) spill accidents to mitigate its damages. Marine Pollution Bulletin, 139, 205-213. https://doi.org/10.1016/j.marpolbul.2018.12.044
  16. Ko, M. K., Jeong, C. H., Lee, M., and Lee, S. H.,2019, Development of a metamodel for predicting near-field propagation of hazardous and noxious substances spilled from a ship. Applied Sciences, 9(18), 3838. https://doi.org/10.3390/app9183838
  17. Kruse, F. A., Boardman, J. W., and Huntington, J. F., 2003, Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1388-1400. https://doi.org/10.1109/TGRS.2003.812908
  18. Lee, M. and Jung, J. Y., 2013, Risk assessment and national measure plan for oil and HNS spill accidents near Korea. Marine Pollution Bulletin, 73(1), 339-344. https://doi.org/10.1016/j.marpolbul.2013.05.021
  19. Lee, M. and Oh, S., 2014, Development of response scenario for a simulated HNS spill incident. Journal of the Korean Society of Marine Environment & Safety, 20(6), 677-684. https://doi.org/10.7837/kosomes.2014.20.6.677
  20. Lee, M. S., Park, K. A., Lee, H. R., Park, J. J., Kang, C. K., and Lee, M., 2016, Detection and dispersion of thick and film-like oil spills in a coastal bay using satellite optical images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(11), 5139-5150. https://doi.org/10.1109/JSTARS.2016.2577597
  21. Luo, G., Chen, G., Tian, L., Qin, K., and Qian, S. E., 2016, Minimum noise fraction versus principal component analysis as a preprocessing step for hyperspectral imagery denoising. Canadian Journal of Remote Sensing, 42(2), 106-116. https://doi.org/10.1080/07038992.2016.1160772
  22. Park, J. J., Oh, S., Park, K. A., Foucher, P. Y., Jang, J. C., Lee, M., and Kang, W. S., 2017, The ship detection using airborne and in-situ measurements based on hyperspectral remote sensing. Journal of the Korean Earth Science Society, 38(7), 535-545. https://doi.org/10.5467/JKESS.2017.38.7.535
  23. Park, J. J., Oh, S., Park, K. A., Kim, T. S., and Lee, M., 2020, Applying hyperspectral remote sensing methods to ship detection based on airborne and ground experiments. International Journal of Remote Sensing, 41(15), 5928-5952. https://doi.org/10.1080/01431161.2019.1707904
  24. Park, J. J., Park, K., Foucher, P. Y., Deliot, P., Floch, S. L., Kim, T. S., and Lee, M., 2021, Hazardous Noxious Substance Detection Based on Ground Experiment and Hyperspectral Remote Sensing. Remote Sensing, 13(2), 318. https://doi.org/10.3390/rs13020318
  25. Park, M. O., Park, H. S., Kim, T., Oh, S., and Lee, M., 2016, A study on the development of HNS database for response system of marine spill accident in Korea. Journal of the Korean Society of Marine Environment and Safety, 22(1), 52-58. https://doi.org/10.7837/kosomes.2016.22.1.052
  26. Randolph, K., Wilson, J., Tedesco, L., Li, L., Pascual, D. L., and Soyeux, E., 2008, Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin. Remote Sensing of Environment, 112(11), 4009-4019. https://doi.org/10.1016/j.rse.2008.06.002
  27. Rodarmel, C. and Shan, J., 2002, Principal component analysis for hyperspectral image classification. Surveying and Land Information Science, 62(2), 115-122.
  28. Seo, D., Shin, S., Oh, S., Lee, M., and Seo, S., 2020, Rapid eco-toxicity analysis of hazardous and noxious substances (HNS) using morphological change detection in Dunaliella tertiolecta. Algal Research, 51, 102063. https://doi.org/10.1016/j.algal.2020.102063
  29. Somers, B., Asner, G. P., Tits, L., and Coppin, P., 2011, Endmember variability in spectral mixture analysis: A review. Remote Sensing of Environment, 115(7), 1603-1616. https://doi.org/10.1016/j.rse.2011.03.003
  30. Sun, S., Lu, Y., Liu, Y., Wang, M., and Hu, C., 2018, Tracking an oil tanker collision and spilled oils in the East China Sea using multisensor day and night satellite imagery. Geophysical Research Letters, 45(7), 3212-3220. https://doi.org/10.1002/2018gl077433
  31. Xiong, W., Chang, C. I., Wu, C. C., Kalpakis, K., and Chen, H. M., 2011, Fast algorithms to implement N-FINDR for hyperspectral endmember extraction. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(3), 545-564. https://doi.org/10.1109/JSTARS.2011.2119466