한국전자통신학회논문지 (The Journal of the Korea institute of electronic communication sciences)

한국전자통신학회 (Korea Institute of Electronic Communication Science)
권호 표지
DOI QR코드

DOI QR Code

GOCI 영상과 기계학습 기법을 이용한 Cochlodinium polykrikoides 적조 탐지 기법 연구

Study on Detection for Cochlodinium polykrikoides Red Tide using the GOCI image and Machine Learning Technique

  • 엥흐자리갈 운자야 (부경대학교 지구환경시스템과학부) ;
  • 박수호 (부경대학교 지구환경시스템과학부) ;
  • 황도현 (부경대학교 지구환경시스템과학부) ;
  • 정민지 (부경대학교 지구환경시스템과학부) ;
  • 김나경 (부경대학교 지구환경시스템과학부) ;
  • 윤홍주 (부경대학교 공간정보시스템공학과)
  • 투고 : 2020.10.30
  • 심사 : 2020.12.15
  • 발행 : 2020.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구에서는 적조 Cochlodinium Polykrikoide를 기계학습 방법과 정지궤도 해색위성 영상을 활용하여 탐지하는 방법을 제안한다. 기계학습 모형을 학습시키기 위해 GOCI Level2 자료를 활용하였으며, 국립수산과학원의 적조 속보 자료를 활용하였다. 기계학습 모델은 로지스틱 회귀모형, 의사결정나무 모형, 랜덤포래스트 모형을 사용하였다. 성능 평가 결과 기계학습을 사용하지 않은 전통적인 GOCI 영상 기반 적조 탐지 알고리즘(Son et al.,2012) (75%)과 비교해보았을 때 약 13~22%p (88~98%)의 정확도 향상을 확인할 수 있었다. 또한 기계학습 모형 간 탐지 성능을 비교 분석해본 결과 랜덤 포레스트 모형(98%)이 가장 높은 탐지 정확도를 보였다. 이러한 기계학습 기반 적조 탐지 알고리즘은 향후 적조를 조기에 탐지하고 그 이동과 확산을 추적 모니터링하는데 활용될 수 있을 것이라고 판단된다.

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

키워드

참고문헌

  1. D. Anderson, P. Anderson, V. Bricelj, J. Cullen, and J. Rensel, Monitoring and Management Strategies for Harmful Algal Blooms in Coastal Waters. Paris: Intergovernmental Oceanographic Commission Technical Series, 2001.
  2. H. Kim, Harmful Algal Blooms in the Sea. Busan: Dasom, 2005.
  3. Y. Yoon, Sea rebellion, Red tide. Paju: Jipmoondang, 2012.
  4. Y. Kim, Y. Byun, Y. Huh, and Y. Yu, "Detection of Cochlodinium polykrikoides Red Tide Using MODIS Level 2 Data in Coastal Waters," Korean Society of Civil Engineers Journal of Civil Engineering, vol. 27, no. 4D, 2007, pp. 535-540.
  5. S. Oh, J. Park, and H. Yoon, "Prediction of Red Tide Occurrence by using Oceanic and Atmospheric Data by Satellite," J. of the Korean institute of Electronic Communication Sciences, vol. 10, no. 2, 2015, pp. 311-318. https://doi.org/10.13067/JKIECS.2015.10.2.311
  6. Y. Ahn, J. Moon, W. Seo, and H. Yoon, "Inherent Optical Properties of Red Tide Algal for Ocean Color Remote Sensing Application," J. of the Korean Society for Marine Environmental Engineering, vol. 12, no. 1, 2009, pp. 47-54.
  7. R. Stumpf, M. Culver, P. Tester, M. Tomlinson, G. Kirkpatrick, B. Pederson, E. Truby, V. Ransibrahmanakul, and M. Soracco, "Monitoring Karenia brevis blooms in the Gulf of Mexico using satellite ocean color imagery and other data," Harmful Algjae, vol. 2, no. 2, 2003, pp. 147-160. https://doi.org/10.1016/S1568-9883(02)00083-5
  8. M. Tomlinson, R. Stumpf, V. Ransibrahmanakul, E. Truby, G. Kirkpatrick, B. Pederson, G. Vargo, and C. Heil, "Evaluation of the use of SeaWiFS imagery for detecting Karenia brevis harmful algal blooms in the eastern Gulf of Mexico," Remote Sensing of Environment, vol. 91, no. 3, 2004, pp. 293-303. https://doi.org/10.1016/j.rse.2004.02.014
  9. Y. Suh, L. Jang, N. Lee, and J. Ishizaka, "Feasibility of Red Tide Detection Around Korean Waters Using Satellite Remote Sensing," J. of Fisheries Science and Technology, vol. 7, no. 3, 2004, pp. 148-162.
  10. J. Ishizaka, Y. Kitaura, Y. Touke, H. Sasaki, A. Tanaka, H. Murakami, T. Suzuki, K. Matsuoka, and H. Nakata, "Satellite Detection of Red Tide in Ariake Sound, 1998-2001," J. of Oceanography, vol. 62, no. 1, 2006, pp. 37-45. https://doi.org/10.1007/s10872-006-0030-1
  11. Y. Son, Y. Kang, and J. Ryu, "Monitoring Red Tide in South Sea of Korea(SSK) Using the Geostationary Ocean Color Imager(GOCI)," Korean J. of Remote Sensing, vol. 26, no. 5, 2012, pp. 531-548.
  12. Y. Ahn and P. Shanmugam, "Detecting the red tide algal bloom from satellite ocean color observations in optically complex Northeast-Asia Coastal waters," Remote Sensing of Environment, vol. 103, no. 4, 2006, pp. 419-437. https://doi.org/10.1016/j.rse.2006.04.007
  13. Y. Son, J. Ishizaka, J. Jeong, H. Kim, and T. Lee, "Cochlodinium polykrikoides red tide detection in the South Sea of Korea using spectral classification of MODIS data," Ocean Science Journal, vol. 46, no. 4, 2011, pp. 239-263. https://doi.org/10.1007/s12601-011-0019-6
  14. S. Bak, H. Kim, D. Hwang, H. Yoon, and W. Seo, "Detection technique of Red tide Using GOCI Level 2 Data," Korean J. Remote Sensing, vol. 32, no. 6, 2016, pp. 673-679. https://doi.org/10.7780/kjrs.2016.32.6.11
  15. S. Bak, H. Kim, D. Hwang, S. Oh, and H. Yoon, "Red Tide Detection Technique by Using Multi-temporal GOCI Level 2 Data," Internation J. of Grid and Distributed Computing, vol. 10, no. 10, 2017, pp. 45-56. https://doi.org/10.14257/ijgdc.2017.10.10.04
  16. H. Kim, S. Jang, and H. Yoon, "Utilization of Unmanned Aerial Vehicle(UAV) Image for Detection of Algal Bloom in Nakdong River," J. of the Korean institute of Electronic Communication Sciences, vol. 12, no. 3, 2017, pp. 457-464.
  17. S. Bak and H. Yoon, "Analysis on optical property in the South Sea of Korea by using Satellite Image : Study of Case on red tide occurrence in August 2013," J. of the Korean institute of Electronic Communication Sciences, vol. 11, no. 7, 2016, pp. 723-728. https://doi.org/10.13067/JKIECS.2016.11.7.723
  18. S. Bak, M. Jeong, D. Hwang, U. Enkhjargal, N. Kim, and H. Yoon, "Study on Cochlodinium polykrikoides Red tide Prediction using Deep Neural Network under Imbalanced Data," J. of the Korean Institute of Electronic Communication Sciences, vol. 14, no. 6, 2019, pp. 1161-1170.
  19. S. Bak, H. Kim, D. Hwang, U. Enkhjargal, B. Kim, and H. Yoon, "Study on Detection Technique for Cochlodinium polykrikoides Red tide using Logistic Regression Model under Imbalanced Data," J. of the Korean Institute of Electronic Communication Sciences, vol. 13, no. 6, 2018, pp. 1353-1363.