KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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Comparison of Feature Point Extraction Algorithms Using Unmanned Aerial Vehicle RGB Reference Orthophoto

무인항공기 RGB 기준 정사영상을 이용한 특징점 추출 알고리즘 비교

  • Lee, Kirim (Kyungpook National University) ;
  • Seong, Jihoon (Kyungpook National University) ;
  • Jung, Sejung (Kyungpook National University) ;
  • Shin, Hyeongil (Kyungpook National University) ;
  • Kim, Dohoon (Kyungpook National University) ;
  • Lee, Wonhee (Kyungpook National University)
  • 이기림 (경북대학교 멀티스케일 유.무기 구조물 자율진단기술연구소) ;
  • 성지훈 (경북대학교 공간정보학과) ;
  • 정세정 (경북대학교 융복합시스템학과) ;
  • 신현길 (경북대학교 융복합시스템학과) ;
  • 김도훈 (경북대학교 융복합시스템학과) ;
  • 이원희 (경북대학교 위치정보시스템학과)
  • Received : 2023.12.04
  • Accepted : 2024.01.22
  • Published : 2024.04.01
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Abstract

As unmanned aerial vehicles(UAVs) and sensors have been developed in a variety of ways, it has become possible to update information on the ground faster than existing aerial photography or remote sensing. However, acquisition and input of ground control points(GCPs) UAV photogrammetry takes a lot of time, and geometric distortion occurs if measurement and input of GCPs are incorrect. In this study, RGB-based orthophotos were generated to reduce GCPs measurment and input time, and comparison and evaluation were performed by applying feature point algorithms to target orthophotos from various sensors. Four feature point extraction algorithms were applied to the two study sites, and as a result, speeded up robust features(SURF) was the best in terms of the ratio of matching pairs to feature points. When compared overall, the accelerated-KAZE(AKAZE) method extracted the most feature points and matching pairs, and the binary robust invariant scalable keypoints(BRISK) method extracted the fewest feature points and matching pairs. Through these results, it was confirmed that the AKAZE method is superior when performing geometric correction of the objective orthophoto for each sensor.

무인항공기와 무인항공기 센서가 다양하게 개발됨에 따라 기존의 항공사진 또는 원격탐사보다 좁은 면적에 대한 정보를 빠르게 업데이트할 수 있다. 하지만 무인항공기 사진측량에서 지상기준점의 획득과 입력은 많은 시간이 소요되며, 지상기준점 측량과 입력이 잘못될 경우 기하 왜곡이 발생한다. 본 연구에서는 이러한 지상기준점 획득과 입력의 시간을 줄이기 위해 RGB 기준 정사영상을 제작하고, 다양한 센서의 목적 정사영상에 특징점 알고리즘을 적용하여 비교·평가를 수행하였다. 연구대상지 2곳에 대해 4가지 특징점 추출 알고리즘을 적용했으며, 그 결과 특징점 대비 매칭쌍의 비율은 speeded up robust features(SURF)가 가장 우수하였다. 전체적으로 비교했을 때 accelerated-KAZE(AKAZE) 방법이 가장 많은 특징점과 매칭쌍을 추출했으며, binary robust invariant scalable keypoints(BRISK) 방법이 가장 적은 특징점과 매칭쌍을 추출했다. 본 결과를 통해 센서별 목적 정사영상 기하보정 수행 시 AKAZE 방법이 우수한 것을 확인할 수 있었다.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(RS-2023-00274068 and NRF-2020R1I1A3061750) and the Korea Institute of Energy Technology Evaluation and Planning (KETEP), the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20224000000150). This paper has been written by modifying and supplementing the KSCE 2023 CONVENTION paper.

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