이전|다음

GF-7 위성 다각도 특성 작물 인식

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

    School of Land Science and Technology, China University of Geosciences, Beijing 100083, China

  • Email：1045376795@qq.com
  • Introduction：孙智虎，研究方向为农业遥感。E-mail： 1045376795@qq.com
SUN Zhihu12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：zhangjs@bnu.edu.cn
  • Introduction：张锦水，研究方向为深度学习与遥感大数据挖掘。E-mail： zhangjs@bnu.edu.cn
ZHANG Jinshui134*,  
  • Affiliation：

    School of Land Science and Technology, China University of Geosciences, Beijing 100083, China

HONG Youtang2,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

YANG Junwen134,  
  • Affiliation：

    Beijing Polytechnic College, Beijing 100042, China

ZHU Shuang5

추상적인

다각도 원격탐사는 보다 풍부하고 다방향의 원격탐사 특성을 제공하여 지형 유형 간 구분성을 향상시키고 지물 피복의 정확한 인식을 위한 견고한 데이터 기반을 마련할 수 있다. GF-7은 중국이 ZY-3 위성 이후 처음으로 발사한 아미터급 측량 위성으로, 다각도 특성을 활용하여 '이물 동일스펙트럼' 문제를 해결하고 작물 식별 정확도를 향상시킬 기회를 제공한다. 본 논문에서는 GF-7 전방위, 후방위 팬크로매틱 및 후방위 다중분광 데이터를 활용하고, 다양한 특성 조합을 서포트 벡터 머신 분류기에 입력하여 분류를 수행하였다. 스펙트럼 및 질감 특성과 비교하여 다각도 특성이 작물 인식 정확도에 미치는 영향을 분석하였다. 결과에 따르면 스펙트럼 특성만 사용할 때와 비교하여 스펙트럼과 각도 차 특성 조합은 마늘과 겨울 밀의 지도 작성 정확도를 각각 4.07% 및 3.15%, 사용자 정확도를 각각 6.73% 및 2.12% 향상시켰다; 스펙트럼 및 질감 특성을 사용할 때와 비교하여 스펙트럼, 질감 및 각도 차 특성 조합은 마늘과 겨울 밀의 지도 작성 정확도를 각각 3.14% 및 1.01%, 사용자 정확도를 각각 5.11% 및 0.67% 향상시켰다. McNemar 검정 분석을 통해 이와 같은 분류 정확도의 향상이 안정적이며 각도 차 특성 사용이 작물 인식 정확도를 효과적으로 높일 수 있음을 확인하였다. 그 원인은 다각도 특성이 다각도 관측 시 작물 유형별로 특유한 스펙트럼 반응 차이를 갖고 있어 작물 간 구분성을 높여 원격탐사 작물 인식 정확도를 보장하기 때문이다.

키워드

GF-7;서포트 벡터 머신;각도 차;원격탐사;겨울 밀;마늘;농업

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