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

Korea Institute of Electronic Communication Science (한국전자통신학회)
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Study on Detection Technique for Cochlodinium polykrikoides Red tide using Logistic Regression Model and Decision Tree Model

로지스틱 회귀모형과 의사결정나무 모형을 이용한 Cochlodinium polykrikoides 적조 탐지 기법 연구

  • 박수호 (부경대학교 지구환경시스템과학부) ;
  • 김흥민 (부경대학교 지구환경시스템과학부) ;
  • 김범규 (부경대학교 지구환경시스템과학부) ;
  • 황도현 (부경대학교 지구환경시스템과학부) ;
  • 엥흐자리갈 운자야 (부경대학교 지구환경시스템과학부) ;
  • 윤홍주 (부경대학교 지구환경시스템과학부)
  • Received : 2018.07.09
  • Accepted : 2018.08.15
  • Published : 2018.08.31
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Abstract

This study propose a new method to detect Cochlodinium polykrikoides on satellite images using logistic regression and decision tree. We used spectral profiles(918) extracted from red tide, clear water and turbid water as training data. The 70% of the entire data set was extracted and used for model training, and the classification accuracy of the model was evaluated by using the remaining 30%. As a result of the accuracy evaluation, the logistic regression model showed about 97% classification accuracy, and the decision tree model showed about 86% classification accuracy.

본 연구에서는 기계학습 기법의 한 갈래인 로지스틱 회귀모형과 의사결정나무 모형을 이용하여 인공위성 영상에서 Cochlodinium polykrikoides 적조 픽셀을 탐지하는 방법을 제안한다. 학습자료로 적조, 청수, 탁수해역에서 추출된 수출광량 분광 프로파일(918개)을 활용하였다. 전체 데이터셋의 70%를 추출하여 모형 학습에 활용하였으며, 나머지 30%를 이용하여 모형의 분류 정확도를 평가하였다. 정확도 평가 결과 로지스틱 회귀모형은 약 97%의 분류 정확도를 보였으며, 의사결정나무 모형은 약 86%의 분류 정확도를 보였다.

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