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GF-7衛星多角度特徴作物識別

  • 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

サマリー

多角度リモートセンシングによる地上観測は、より豊富で多方向のリモートセンシング特徴を提供し、地物分類間の識別性を高め、地物被覆の精密な識別のための堅固なデータ基盤を築くことができる。GF-7は中国のZY-3衛星に続く初の亜メートル級測量衛星であり、多角度特性を活用して「異物同スペクトル」の問題を解決し、作物識別精度を向上させる機会をもたらす。本研究ではGF-7の前方視、後方視のパンクロマティックおよび後方視マルチスペクトルデータを利用し、様々な特徴組み合わせをサポートベクターマシン分類器に入力して分類を行った。スペクトルやテクスチャなどの特徴と比較して、多角度特徴が作物識別精度に及ぼす影響を分析した。結果は、スペクトル特徴のみの場合と比較して、スペクトルと角度差特徴の組み合わせによりニンニクと冬小麦のマッピング精度がそれぞれ4.07%、3.15%向上し、ユーザー精度はそれぞれ6.73%、2.12%向上したことを示している。スペクトルとテクスチャ特徴の場合と比較して、スペクトル、テクスチャおよび角度差特徴の組み合わせによりニンニクと冬小麦のマッピング精度がそれぞれ3.14%、1.01%、ユーザー精度はそれぞれ5.11%、0.67%向上した。McNemar検定分析により、この分類精度の向上は安定しており、角度差特徴の使用は作物の識別精度を効果的に高めることが確認された。これは、多角度特徴が多角度観測時に異なる作物タイプのスペクトル応答に独特な違いを持ち、この違いが作物間の識別性を高め、リモートセンシングによる作物識別の精度を保証するためである。

キーワード

GF-7;サポートベクターマシン;角度差;リモートセンシング;冬小麦;ニンニク;農業

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