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深層学習に基づく全偏波SAR画像の氷河境界識別

  • role： First author第一作者
  • Affiliation：

    School of Geographic and Oceanographic Science, Nanjing University, Nanjing 210000, China

  • Email：734408513@qq.com
  • Introduction：范吉延，研究方向为极地遥感影像识别。E-mail：734408513@qq.com
FAN Jiyan,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geographic and Oceanographic Science, Nanjing University, Nanjing 210000, China

  • Email：kecq@nju.deu.cn
  • Introduction：柯长青，研究方向为冰雪与水资源遥感，E-mail：kecq@nju.deu.cn
KE Changqing*,  
  • Affiliation：

    School of Geographic and Oceanographic Science, Nanjing University, Nanjing 210000, China

YAO Guohui,  
  • Affiliation：

    School of Geographic and Oceanographic Science, Nanjing University, Nanjing 210000, China

WANG Zifei

サマリー

氷河の識別は周辺地域の水資源および気候変動の監視に重要な意義を持つ。全偏波SAR画像は地物表面散乱、偶次散乱、体散乱、統計的特性など豊富な特徴を含み、深層学習は画像情報を十分に活用できる。したがって、全偏波SAR画像と深層学習を組み合わせて使用することで精度の高い氷河識別効果が得られる。本論文はヒマラヤ山脈西端のALOS2-PALSAR全偏波画像を基に、VGG16特徴抽出ネットワークと全畳み込み神経ネットワークモデルU-netを組み合わせたVGG16-unetで氷河を識別した。使用した特徴は偏波相関行列の対角成分、Freeman-Durden、H/A/α、Pauli、VanZyl、Yamaguchiの5種類の偏波分解パラメータ、合計19の特徴である。画像情報を十分に活用するため、これらの特徴を分析および組み合わせ、氷河識別精度を比較して最適な特徴を選択した。氷河と非氷河の地形が明確に異なるため、DEM、傾斜度、局所入射角などを補助特徴として偏波特徴と組み合わせた。異なる偏波特徴分類精度の比較により、物理特性に基づくPauli、Freeman-Durden、VanZyl、Yamaguchi特徴の分類精度が高く、特にPauli特徴分類の精度が最も高く、全体精度（OA）は92.54%、平均ユーザー交差率（mIoU）は78.78%に達した。地形データを加えると全体精度（OA）は94.34%、平均ユーザー交差率（mIoU）は82.35%に向上した。さらに氷河識別精度を高めるため、単一波長特徴の全体精度（OA）および再現率（Recall）に基づいて選定されたSDV（表面散乱、偶次散乱、体散乱）特徴の交差組み合わせ方式を提案し、結果はこの組み合わせが全体精度（OA）94.98%、ユーザー交差率（mIoU）85.67%に達し、Pauli特徴分類精度よりそれぞれ0.64%、3.32%高いことを示した。以上の結果は、最適な特徴組み合わせ方式の選択と深層学習の組み合わせが氷河識別精度の向上に重要な役割を果たすことを示している。

キーワード

リモートセンシング;氷河;ALOS2-PALSAR;偏波分解;画像分割;深層学習;ヒマラヤ

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