이전|다음

분수차분 기반 토양 중금속 고분광 원격탐사 영상 역산

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：dingsongtao20@mails.ucas.ac.cn
  • Introduction：丁松滔，研究方向为土壤重金属高光谱遥感反演。E-mail： dingsongtao20@mails.ucas.ac.cn
DING Songtao12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：zhangxia@radi.ac.cn
  • Introduction：张霞，研究方向为高光谱定量遥感研究。E-mail： zhangxia@radi.ac.cn
ZHANG Xia1*,  
  • Affiliation：

    Land Satellite Remote Sensing Applications Center, MNR, Beijing 100048, China

SHANG Kun3,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

LI Ru1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

SUN Weichao1

추상적인

고분광 영상 기술은 저비용으로 광범위한 토양 중금속의 신속한 모니터링을 실현하는 데 독특한 잠재력을 가지고 있다. 고분광 영상 역산에서 두드러진 소규모 샘플 문제를 해결하기 위해 본 연구에서는 분수차분(Fractional Order Derivative, FOD)을 기반으로 한 토양 중금속 역산 방법을 제안하였다. 먼저, 토양 샘플링 지점의 인접 픽셀을 이용하여 샘플을 확장하고, 샘플의 스펙트럼 차이를 증가시켰다. 다음으로, FOD를 사용하여 미분 스펙트럼의 그라디언트 정보를 유지하면서 스펙트럼 특징을 부각시켰다. 이어서 경쟁 적응 재가중 샘플링(Competitive Adaptive Reweighted Sampling, CARS)을 통해 최적 대역을 선정하고, 편미분 최소제곱법(Partial Least Squares Regression, PLSR)을 사용하여 역산 모델을 구축하였다. 연구 데이터는 신장 위구르 자치구 하미시 황산 남광구에서 획득한 72개의 토양 샘플과 항공 고분광 영상으로, 납(Pb), 아연(Zn), 니켈(Ni) 세 가지 중금속에 대해 역산하였다. 결과는 샘플 확장이 모델 과적합 현상을 완화시켰을 뿐만 아니라 중금속 역산 정확도를 향상시켰음을 보여준다. 최적 차수의 분수차분은 스펙트럼 특징을 효과적으로 강화하여 역산 정확도를 높였다. CARS는 상관 계수(CC) 방법 및 유전 알고리즘(GA)과 비교할 때 우수한 대역 조합 선택으로 연구 지역 내 Pb, Zn, Ni 중금속 역산 정확도가 각각 R² 0.7974, 0.8690, 0.8303로 나타났으며, 역산 방법의 견고성을 입증하였다.

키워드

분수차분;고분광 원격탐사 영상;CARS;토양 중금속;소규모 샘플;가시광선 근적외선;단파 적외선

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