Journal of Korean Society of Forest Science (한국산림과학회지)

Korean Society of Forest Science (한국산림과학회)
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A Study on the Performance of Deep learning-based Automatic Classification of Forest Plants: A Comparison of Data Collection Methods

데이터 수집방법에 따른 딥러닝 기반 산림수종 자동분류 정확도 변화에 관한 연구

  • Kim, Bomi (Chungnam Forest Environment Research Institute) ;
  • Woo, Heesung (School of Forestry Sciences and Landscape Architecture, Kyungpook National University) ;
  • Park, Joowon (School of Forestry Sciences and Landscape Architecture, Kyungpook National University)
  • 김보미 (충청남도 산림자원연구소) ;
  • 우희성 (경북대학교 산림과학.조경학부) ;
  • 박주원 (경북대학교 산림과학.조경학부)
  • Received : 2019.11.22
  • Accepted : 2020.03.09
  • Published : 2020.03.31
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Abstract

The use of increased computing power, machine learning, and deep learning techniques have dramatically increased in various sectors. In particular, image detection algorithms are broadly used in forestry and remote sensing areas to identify forest types and tree species. However, in South Korea, machine learning has rarely, if ever, been applied in forestry image detection, especially to classify tree species. This study integrates the application of machine learning and forest image detection; specifically, we compared the ability of two machine learning data collection methods, namely image data captured by forest experts (D1) and web-crawling (D2), to automate the classification of five trees species. In addition, two methods of characterization to train/test the system were investigated. The results indicated a significant difference in classification accuracy between D1 and D2: the classification accuracy of D1 was higher than that of D2. In order to increase the classification accuracy of D2, additional data filtering techniques were required to reduce the noise of uncensored image data.

최근 급변하는 컴퓨터 기술의 발전을 통해 컴퓨터 비전과 머신러닝을 이용한 사물인식 기법이 다양한 학문 분야에서 사용되고 있다. 국내의 연구 사례를 보면 주로 대면적 산림을 분석하기 위한 이미지 학습 및 객체인식 기법이 사용되는 반면 개체목 단위의 수종 분류 및 특징을 학습하는 연구는 아직 미미한 실정이다. 이에 본 연구는 한국의 침엽수 5종을 대상으로 이미지 학습을 통한 자동분류 연구의 가능성을 분석해 보았다. 데이터 형태에 따른 분류 결과의 차이를 분석하기 위하여 산림전문가가 직접 촬영한 영상(D1)과 웹크롤링을 이용한 영상(D2)을 사용하여 수종 분류를 실시하였다. 그 결과 D1과 D2의 분류 정확도에 유의미한 차이가 있는 것으로 나타났으며, D1은 D2보다 높은 분류 정확도를 나타냈다. 또한, D2의 분류 정확도를 높이기 위해서는 검열되지 않은 영상 데이터의 노이즈를 줄이기 위한 추가 데이터 필터링 기법이 필요한 것으로 사료된다.

Keywords

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