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Inversion hyperspectrale des métaux lourds dans les sols basée sur la dérivée d'ordre fractionnaire

  • 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

résumé

La technologie d'imagerie hyperspectrale possède un potentiel unique pour réaliser une surveillance rapide et à faible coût des métaux lourds dans les sols à grande échelle. Pour résoudre le problème des petits échantillons dans l'inversion des images hyperspectrales, cet article propose une méthode d'inversion des métaux lourds des sols basée sur la dérivée d'ordre fractionnaire (Fractional Order Derivative, FOD). Tout d'abord, l'expansion des échantillons est effectuée en utilisant les pixels voisins des points d'échantillonnage du sol pour augmenter la variabilité spectrale des échantillons ; ensuite, la FOD est utilisée pour mettre en valeur les caractéristiques spectrales tout en conservant les informations du gradient du spectre différentiel ; puis, la méthode de sélection compétitive adaptative de rééchantillonnage pondéré (Competitive Adaptive Reweighted Sampling, CARS) est employée pour sélectionner les bandes optimales, et le modèle d'inversion est construit par la méthode des moindres carrés partiels (Partial Least Squares Regression, PLSR). Les données de l'étude comprennent 72 échantillons de sol et des images hyperspectrales aériennes obtenus dans la zone minière du sud de Huangshan, ville de Hami, Région autonome ouïghoure du Xinjiang, pour l'inversion de trois métaux lourds : plomb (Pb), zinc (Zn) et nickel (Ni). Les résultats montrent que l'expansion des échantillons atténue non seulement le surapprentissage du modèle, mais améliore également la précision de l'inversion des métaux lourds ; la dérivée fractionnaire d'ordre optimal renforce efficacement les caractéristiques spectrales et améliore la précision de l'inversion ; comparée à la méthode du coefficient de corrélation (CC) et à l'algorithme génétique (GA), la sélection des bandes par CARS offre une meilleure précision d'inversion des métaux lourds Pb, Zn, Ni dans la zone d'étude avec des R² respectifs de 0,7974, 0,8690 et 0,8303, ce qui démontre une bonne robustesse de la méthode.

mots-clés

dérivée d'ordre fractionnaire;imagerie hyperspectrale;CARS;métaux lourds dans les sols;petit échantillon;visible et proche infrarouge;infrarouge à ondes courtes

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