[논문]국내학회지 논문 리뷰를 통한 원격탐사 분야 딥러닝 연구 동향 분석

이창희; 윤예린; 배세정; 어양담; 김창재; 신상호; 박소영; 한유경

doi:10.7848/ksgpc.2021.39.6.437

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 원문보기

한국측량학회지 = Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, v.39 no.6, 2021년, pp.437 - 456

이창희 (Dept. of Civil Engineering, Seoul National University of Science and Technology) , 윤예린 (Dept. of Civil Engineering, Seoul National University of Science and Technology) , 배세정 (School of Civil Engineering, Seoul National University of Science and Technology) , 어양담 (Dept. of Civil and Environmental Engineering, Konkuk University) , 김창재 (Dept. of Civil and Environmental Engineering, Myongji University) , 신상호 (Geographic Information Division, National Geographic Information Institute, Ministry of Land, Infrastructure and Transport) , 박소영 (Geographic Information Division, National Geographic Information Institute, Ministry of Land, Infrastructure and Transport) , 한유경 (Dept. of Civil Engineering, Seoul National University of Science and Technology)

초록
AI-Helper

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.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (12)

그림 Fig. 1. Number of deep learning papers in the remote sensing field by presentation year (investigated until October 2021)
그림 Fig. 2. Number of deep learning papers in the remote sensing field distributed by Korean domestic journal (investigated until October 2021)
그림 Fig. 3. Number of deep learning papers in the remote sensing field distributed by purpose of deep learning network (investigated until October 2021)
그림 Fig. 4. Number of deep learning papers in the remote sensing field distributed by research topic (investigated until October 2021)
그림 Fig. 5. Number of deep learning papers in the remote sensing field distributed by image acquisition platform (investigated until October 2021)
그림 Fig. 6. Architecture of AlexNet(Krizhevsky et al., 2012)
그림 Fig. 7. Architecture of Faster R-CNN network
그림 Fig. 8. Architecture of YOLO v1 network(Redmon et al., 2016)
그림 Fig. 9. Comparison of scene classification, object detection, and semantic segmentation (Kim et al., 2019)
그림 Fig. 10. Architecture of U-Net network (Ronneberger et al., 2015)
그림 Fig. 11. Architecture of GAN network
표 Table. 1. Comparison of deep learning research according to the remote sensing platforms

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저자의 다른 논문 :

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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