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Инверсия гиперспектральных изображений тяжелых металлов в почве на основе дробного дифференцирования

  • 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

реферат

Технология гиперспектрального изображений обладает уникальным потенциалом для низкозатратного масштабного быстрого мониторинга тяжелых металлов в почвах. Для решения проблемы малой выборки, характерной для инверсии гиперспектральных изображений, в данной работе предложен метод инверсии тяжелых металлов в почве на основе дробного дифференцирования (Fractional Order Derivative, FOD). Во-первых, для расширения выборки используются соседние пиксели точек отбора проб почвы, что увеличивает спектральное разнообразие выборки; затем с помощью FOD выделяются спектральные признаки при сохранении информации о градиенте дифференциального спектра; далее с помощью конкурентной адаптивной повторной выборки (Competitive Adaptive Reweighted Sampling, CARS) отбираются оптимальные полосы, и строится модель инверсии методом частичных наименьших квадратов (Partial Least Squares Regression, PLSR). В качестве данных исследования использовались 72 образца почвы и воздушные гиперспектральные изображения с южного рудника Хуаншань города Хами в Синьцзян-Уйгурском автономном районе для инверсии трех тяжелых металлов: свинца (Pb), цинка (Zn) и никеля (Ni). Результаты показывают, что расширение выборки не только смягчает переобучение модели, но и повышает точность инверсии тяжелых металлов; дробное дифференцирование оптимального порядка эффективно усиливает спектральные признаки и улучшает точность инверсии; по сравнению с методом коэффициента корреляции (CC) и генетическим алгоритмом (GA), отбор полос с помощью CARS обеспечивает лучшую точность инверсии тяжелых металлов Pb, Zn, Ni в исследуемом районе с коэффициентами R² 0.7974, 0.8690 и 0.8303 соответственно, что свидетельствует о хорошей устойчивости метода.

ключеви́че слова́

дробное дифференцирование;гиперспектральные изображения;CARS;тяжелые металлы в почве;малая выборка;видимое и ближнее инфракрасное излучение;коротковолновое инфракрасное излучение

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