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 under Imbalanced Data

불균형 데이터 환경에서 로지스틱 회귀모형을 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 : 2018.11.09
  • Accepted : 2018.12.15
  • Published : 2018.12.31
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Abstract

This study proposed a method to detect Cochlodinium polykrikoides red tide pixels in satellite images using a logistic regression model of machine learning technique under Imbalanced data. The spectral profiles extracted from red tide, clear water, and turbid water were used as training dataset. 70% of the entire data set was extracted and used for as model training, and the classification accuracy of the model was evaluated using the remaining 30%. At this time, the white noise was added to the spectral profile of the red tide, which has a relatively small number of data compared to the clear water and the turbid water, and over-sampling was performed to solve the unbalanced data problem. As a result of the accuracy evaluation, the proposed algorithm showed about 94% classification accuracy.

본 연구에서는 불균형 데이터 환경에서 기계학습 기법의 한 갈래인 로지스틱 회귀모형을 이용하여 인공위성 영상에서 Cochlodinium polykrikoides 적조 픽셀을 탐지하는 방법을 제안한다. 학습자료로 적조, 청수, 탁수 해역에서 추출된 수출광량 분광 프로파일을 활용하였다. 전체 데이터셋의 70%를 추출하여 모형 학습에 활용하였으며, 나머지 30%를 이용하여 모형의 분류 정확도를 평가하였다. 이 때, 청수와 탁수에 비해 자료 수가 상대적으로 적은 적조의 분광 프로파일에 백색 잡음을 추가하여 오버샘플링을 하여 불균형 데이터 문제를 해결하였다. 정확도 평가 결과 본 연구에서 제안하는 알고리즘은 약 94%의 분류 정확도를 보였다.

Keywords

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그림 1. 연구 흐름도 Fig. 1 Flow chart of research

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그림 2. 로지스틱 회귀모형을 활용한 적조탐지 과정 Fig. 2 Process of Red tide detection using Logistic regression model

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그림 3. 적조 미발생 기간에 적조로 탐지된 적조 픽셀 수의 상대적 차이(A : Bak et al., 2018, B : 제안하는 알고리즘). Fig. 3 The relative difference in the number of red tide pixels detected as red tides during the no red tide occurrence period(A : Bak et al., 2018, B : Proposed Algorithm).

표 1. GOCI 밴드 구성 Table 1. Band Composition of GOCI

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표 2. 혼동행렬 Table 2. Confusion Matrix

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표 3. 언더샘플링을 적용한 로지스틱 회귀모형[21]의 혼동행렬 Table 3. Confusion Matrix of Logistic Regression Model applied under-sampling[21]

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표 4. 제안하는 알고리즘의 혼동행렬 Table 4. Confusion Matrix of Proposed algorithm

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