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分類後確率空間に基づく双子Nested-UNet（SNU-PS）変化検出ネットワーク

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

    Beijing Municipal Engineering and Technology Center for Land Surface Remote Sensing Data Products, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

  • Email：zhuchuanhai@mail.bnu.edu.cn
  • Introduction：朱传海，研究方向为遥感变化检测与灾情监测。E-mail： zhuchuanhai@mail.bnu.edu.cn
ZHU Chuanhai,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

    Beijing Municipal Engineering and Technology Center for Land Surface Remote Sensing Data Products, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

  • Email：chenxuehong@bnu.edu.cn
  • Introduction：陈学泓，研究方向为遥感数据融合、变化检测。E-mail： chenxuehong@bnu.edu.cn
CHEN Xuehong*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

    Beijing Municipal Engineering and Technology Center for Land Surface Remote Sensing Data Products, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

CHEN Jin,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

    Beijing Municipal Engineering and Technology Center for Land Surface Remote Sensing Data Products, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

YUAN Yuheng,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

    Beijing Municipal Engineering and Technology Center for Land Surface Remote Sensing Data Products, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

TANG Kai

サマリー

近年、深層学習は複数時相リモートセンシング画像の変化検出タスクにおいて大きな可能性を示しています。十分な学習サンプルは、深層学習技術がリモートセンシング画像の変化特徴を効果的に抽出するための重要な前提条件です。しかし、現在の限られた公開アノテーションデータセットは、実際の応用におけるさまざまな変化タイプの検出ニーズを満たしていません。地表被覆の変化は通常、領域のごく一部を占めるため、取得可能な変化サンプルは非常に少なく、変化しないサンプルと比較して深刻な不均衡問題があります。したがって、少数サンプルかつ不均衡な状況下で変化検出ネットワークを効果的に学習させる方法は急務の課題です。変化検出サンプルと比較すると、単時相地表被覆分類サンプルは取得がはるかに容易であり、分類サンプルの支援のもと、十分に学習された地表被覆分類ネットワークは変化検出に重要な事前特徴を提供できます。これに基づき、本稿では分類後確率空間に基づく双子Nested-UNet変化検出ネットワークSNU-PS（Siamese Nested-UNet for change detection in Posterior Probability Space）を提案し、2時相の地表被覆分類後確率情報を組み合わせることで変化検出サンプルへの依存を軽減します。本手法はまず地表被覆分類サンプルを用いて高解像度ネットワークHRNet（High-Resolution Network）を学習し、2時相画像の物体分類後確率を得ます。次に後確率画像を双子Nested-UNet変化検出ネットワークSNU（Siamese Nested-UNet for change detection）に入力し、変化検出結果を取得します。SpaceNet7およびHRSCDデータセットでのテスト結果は、SNU-PSが地表被覆のセマンティック情報を十分に活用し、異なる変化検出学習サンプル数の水準で安定した変化検出精度を維持できることを示しています。分類後比較PCC（Post Classification Comparison）、後確率空間に基づく変化ベクトル解析CVAPS（Change-vector analysis in posterior probability space）、および様々なタイプの変化検出ネットワーク（SNU、FC-EF、BIT、PCFN）と比較して、より高く安定した変化検出精度を有し、特にサンプル数が不足する場合に優位性が明らかです。したがって、本稿で提案するSNU-PSは少数サンプル環境下での変化検出タスクに対してより良い応用展望を有します。

キーワード

地表被覆;変化検出;深層学習;少数サンプル;サンプル不均衡;セマンティックセグメンテーションネットワーク;双子ネットワーク;後確率

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