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分数階微分に基づく土壌重金属ハイパースペクトルリモートセンシング画像の反演

  • 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

サマリー

ハイパースペクトルイメージング技術は、低コストかつ広範囲での土壌中の重金属の迅速なモニタリングに独自の潜在能力を持つ。ハイパースペクトル画像の反演において顕著な少数サンプル問題に対し、本研究は分数階微分（Fractional Order Derivative、FOD）に基づく土壌重金属反演手法を提案する。まず、土壌採取点の近接画素を用いてサンプルを拡張し、サンプルのスペクトル差異を増加させる。次に、FODを用いて微分スペクトルの勾配情報を保持しつつスペクトル特徴を強調する。さらに、競合適応再重み付けサンプリング（Competitive Adaptive Reweighted Sampling、CARS）により波長帯を最適選択し、部分最小二乗法（Partial Least Squares Regression、PLSR）で反演モデルを構築する。研究データは新疆ウイグル自治区ハミ市黄山南鉱区で得られた72個の土壌サンプルと航空ハイパースペクトル画像を用い、鉛（Pb）、亜鉛（Zn）、ニッケル（Ni）の3種の重金属の反演を行った。結果は、サンプル拡張によりモデルの過学習現象が緩和され、重金属反演精度が向上したことを示す。最適階数の分数階微分はスペクトル特徴を効果的に強化し、反演精度を高めた。CARSは相関係数法（CC）や遺伝的アルゴリズム（GA）と比較して選択した波長帯の組み合わせによる反演精度が優れており、研究地域のPb、Zn、Niの反演精度はそれぞれR²が0.7974、0.8690、0.8303であり、本反演手法の頑健性を示している。

キーワード

分数階微分;ハイパースペクトルリモートセンシング画像;CARS;土壌重金属;小サンプル;可視近赤外;短波赤外

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