특징 우선 선택을 고려한 항공 LiDAR 심도 해저 점군 퇴적물 분류

  • role: First author第一作者
  • Affiliation:

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

    Key Laboratory of Ocean Geomatics, Ministry of Natural Resources of China, Qingdao 266590, China

    Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

  • Email:sudianpeng@126.com
  • Introduction:宿殿LiDARE-mail sudianpeng@126.com
SU Dianpeng123,  
  • Affiliation:

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

HUANG Yu1,  
  • role: Corresponding author通信作者
  • Affiliation:

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

    Key Laboratory of Ocean Geomatics, Ministry of Natural Resources of China, Qingdao 266590, China

  • Email:flyang@126.com
  • Introduction: E-mail flyang@126.com
YANG Fanlin12*,  
  • Affiliation:

    Key Laboratory of Ocean Geomatics, Ministry of Natural Resources of China, Qingdao 266590, China

    Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

ZHAO Dineng23,  
  • Affiliation:

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

    Key Laboratory of Ocean Geomatics, Ministry of Natural Resources of China, Qingdao 266590, China

YANG Anxiu12,  
  • Affiliation:

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

LIU Jiaoyang1

추상적인

항공 LiDAR 심도 측정 ALB(Airborne LiDAR Bathymetry) 기술을 기반으로 한 해저 퇴적물 분류는 연안 해역의 해양 자원 개발, 해양 환경 보호, 해양 공학 건설 등에 기초 데이터를 제공할 수 있으며, 해양 활동과 해양 과학 연구에 중요한 의미를 갖는다. ALB 해저 퇴적물 분류에서 특징 중복 문제를 해결하기 위해, 본 논문은 파형 및 지형 특징 우선 선택을 고려한 퇴적물 분류 알고리즘을 제안한다. 파형 및 지형 특징 추출을 기반으로 Relief-F 특징 우선 선택 모델을 구축하여 각 특징이 퇴적물 분류에 미치는 기여도를 계산함으로써 다변량 특징 우선 선택을 실현한다; 그 후, 랜덤 포레스트(RF), 서포트 벡터 머신(SVM), BP 신경망(BPNN) 3종의 분류기를 이용해 감독 학습 분류를 수행하여 산호초, 자갈, 모래, 식생, 해안대 5가지 퇴적물을 추출한다. 제안한 분류 방법의 유효성을 검증하기 위해 시사 강천도(ALB) 실측 데이터를 이용하여 실험을 수행하였으며, 결과는 Relief-F 알고리즘으로 특징 우선 선택 후 RF, SVM, BPNN의 분류 정확도가 각각 1.1%, 1.1%, 2.7% 향상되었음을 보여준다; 이 중 랜덤 포레스트 분류가 더 높은 분류 정확도를 가지며, 전체 정확도 OA(Overall Accuracy)와 카파 계수가 각각 95.36%와 0.94에 도달하였다. 본 연구 성과는 해양 공학 등 분야의 해저 퇴적물 분류 요구에 효과적인 기술 지원을 제공할 수 있다.

키워드

항공 LiDAR 심도 측정;퇴적물 분류;파형 특징;지형 특징;Relief-F 특징 우선 선택 모델;영상 처리;해양

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