이전|다음

특징 선택 및 랜덤 포레스트 알고리즘 융합 기반 GF-6 영상의 동북 단기 벼 원격 감지 추출

  • role： First author第一作者
  • Affiliation：

    College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China

  • Email：yueqizhang1997@126.com
  • Introduction：张悦琦，研究方向为生态环境遥感。E-mail：yueqizhang1997@126.com
ZHANG Yueqi,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China

  • Email：renhongrui@tyut.edu.cn
  • Introduction：任鸿瑞，研究方向为植被遥感。E-mail：renhongrui@tyut.edu.cn
REN Hongrui*

추상적인

고효율, 고정밀의 동북지역 단기 벼 재배 면적 추출 방법을 모색하기 위해 본 연구는 랴오닝성 판진시를 연구구역으로 설정하였으며, 벼의 주요 생육기기를 포함하는 6장의 GF-6 WFV 단일시상 영상과 시계열 영상을 이용하였다. 스펙트럼 특성, 식생 지수, 수체 지수 및 레드엣지 지수 4종 특성 변수를 구성하였고, 평균 불순도 감소법으로 중요도 순위를 정한 후 가방 외 오류법을 통해 최적 입력 특성을 선택하였다. 선택 특징 기반 랜덤 포레스트 모델을 구축하여 2020년 판진시 벼 재배 분포를 추출하였다. 결과는 다음과 같다: (1) 벼의 다양한 생육기에 따른 단일시상 영상 기반 분류 정확도는 전체적으로 94% 이상이며, 벼 이식기 영상 분류 결과가 가장 우수하여 전체 정확도, 벼 F1 값, 카파 계수 및 현장 검증 점수는 각각 97.67%, 98.84%, 0.97, 97.22%였다. (2) 단일시상 영상과 비교하여 시계열 영상을 활용한 토지피복 분류 및 벼 정보 추출은 분류 정확도를 효과적으로 향상시켜 전체 정확도 99.33%, 벼 F1 값 100.00%, 카파 계수 0.99 및 현장 검증 점수 97.22%를 나타냈다. (3) 레드엣지 정보 포함 여부에 따른 벼 추출 결과 비교 분석에서 레드엣지 밴드 및 지수 도입은 분류 정확도를 높였다. (4) 퍼플엣지 및 옐로우엣지 밴드 도입은 분류 정확도를 높였으나 레드엣지 정보보다는 효과가 미흡했다. 본 연구는 특징 선택 기반 랜덤 포레스트 모델이 벼 이식기 단일시상 영상을 활용해 벼 재배 분포를 추출하는 데 있어 실제 적용 정확도 요구를 충족함을 입증하였으며, 시계열 영상을 통해 분류 정확도를 더욱 향상시킬 수 있음을 보여준다. 또한 GF-6 위성의 신규 밴드는 벼 분류 정확도를 개선할 수 있어 GF-6 위성이 작물 세밀 추출에 큰 응용 잠재력을 지님을 나타낸다.

키워드

원격 감지;랜덤 포레스트;레드엣지 밴드;특징 선택;가오펀-6;벼;퍼플엣지 밴드;옐로우엣지 밴드

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