한국측량학회지 (Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)

  • 제39권6호
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  • Pages.437-456
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  • 2021
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  • 1598-4850(pISSN)
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  • 2288-260X(eISSN)
한국측량학회 (Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)
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국내학회지 논문 리뷰를 통한 원격탐사 분야 딥러닝 연구 동향 분석

Analysis of Deep Learning Research Trends Applied to Remote Sensing through Paper Review of Korean Domestic Journals

  • Lee, Changhui (Dept. of Civil Engineering, Seoul National University of Science and Technology) ;
  • Yun, Yerin (Dept. of Civil Engineering, Seoul National University of Science and Technology) ;
  • Bae, Saejung (School of Civil Engineering, Seoul National University of Science and Technology) ;
  • Eo, Yang Dam (Dept. of Civil and Environmental Engineering, Konkuk University) ;
  • Kim, Changjae (Dept. of Civil and Environmental Engineering, Myongji University) ;
  • Shin, Sangho (Geographic Information Division, National Geographic Information Institute, Ministry of Land, Infrastructure and Transport) ;
  • Park, Soyoung (Geographic Information Division, National Geographic Information Institute, Ministry of Land, Infrastructure and Transport) ;
  • Han, Youkyung (Dept. of Civil Engineering, Seoul National University of Science and Technology)
  • 투고 : 2021.11.23
  • 심사 : 2021.12.10
  • 발행 : 2021.12.31
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⟨ 이전 논문 다음 논문 ⟩

초록

우리나라 원격탐사 분야에서는 2017년을 기점으로 딥러닝의 뛰어난 성능을 바탕으로 연구 성과를 나타내기 시작하여, 현재는 영상 전처리부터 활용까지 원격탐사의 거의 모든 분야에서 딥러닝을 적용하는 연구가 수행되고 있다. 원격탐사 분야에 적용된 딥러닝의 연구 동향 분석을 수행하기 위해, 2021년 10월까지 출판된 원격탐사 분야에 딥러닝이 적용된 국내 논문들을 수집하였다. 수집된 60여 편의 논문들을 바탕으로 딥러닝 네트워크 목적, 원격탐사 활용 분야, 원격탐사 영상 취득 탑재체별로 나누어 연구 동향 분석을 수행하였다. 또한, 논문에서 훈련자료 구축에 효과적으로 이용되었던 오픈소스데이터들을 정리하였다. 본 논문을 통해 현시점에서 딥러닝이 원격탐사 분야에 자리잡기 위해 해결해야 할 문제점들을 제시하면서, 향후 연구자들의 원격탐사 분야에 딥러닝 기술을 접목하기 위한 연구 방향을 설정하는 데 도움을 제공하고자 한다.

In the field of remote sensing in Korea, starting in 2017, deep learning has begun to show efficient research results compared to existing research methods. Currently, research is being conducted to apply deep learning in almost all fields of remote sensing, from image preprocessing to applications. To analyze the research trend of deep learning applied to the remote sensing field, Korean domestic journal papers, published until October 2021, related to deep learning applied to the remote sensing field were collected. Based on the collected 60 papers, research trend analysis was performed while focusing on deep learning network purpose, remote sensing application field, and remote sensing image acquisition platform. In addition, open source data that can be effectively used to build training data for performing deep learning were summarized in the paper. Through this study, we presented the problems that need to be solved in order for deep learning to be established in the remote sensing field. Moreover, we intended to provide help in finding research directions for researchers to apply deep learning technology into the remote sensing field in the future.

키워드

과제정보

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아수행된 연구임(No. 2021R1A2C2093671). 이 논문은 2021년도 국토지리정보원의 '항공영상 품질검사 자동화체계 연구'사업의 지원을 받아 수행된 연구임.

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