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Gaofen-1 Breitband Mehrphasiger Gewässerextraktionsdatensatz

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094

    Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing 100190, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences,Beijing 100049

  • Email：wangxingbin23@mails.ucas.ac.cn
  • Introduction：王兴斌，研究方向为遥感图像处理。E-mail：wangxingbin23@mails.ucas.ac.cn
Wang Xingbin123,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094

    Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing 100190, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences,Beijing 100049

Zhou Guangyao123,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094

    Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing 100190, China

Zhang Peng12,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094

    Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing 100190, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences,Beijing 100049

Ye Jinzhou123,  
  • Affiliation：

    Department of Geography, The University of Hong Kong

Zhang Hongsheng4,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094

    Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing 100190, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences,Beijing 100049

Geng Xiurui123,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094

    Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing 100190, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences,Beijing 100049

  • Email：jily@mail.ustc.edu.cn
  • Introduction：计璐艳，研究方向为陆表水体制图。E-mail：jily@mail.ustc.edu.cn
Ji Luyan123*

Resümee

Aufgrund der Einschränkungen der derzeit öffentlich zugänglichen Wasserdatenbestände, die nur eine einzelne Zeitphase aufweisen und eine geringe Annotationsgenauigkeit haben, erstellt diese Studie einen hochwertigen Datensatz zur mehrphasigen Seenextraktion basierend auf den breitbandigen multispektralen Bildern des "Gaofen-1" (GF-1). Es wurden drei Testgebiete ausgewählt: der Poyang-See mit hoher Dynamik, der Namco-See mit mittlerer Dynamik und der Yangcheng-See mit geringer Dynamik, die die vier Jahreszeiten Frühling, Sommer, Herbst und Winter 2022 abdecken. Zur Verbesserung der Datenqualität wurden die breitbandigen multispektralen GF-1-Daten zunächst radiometrisch, orthorektifiziert und schnell atmosphärisch korrigiert; anschließend wurde eine Beschriftungsstrategie kombiniert aus automatischer und manueller visueller Kontrolle angewandt, wobei die Gesamtgenauigkeit in den drei Untersuchungsgebieten und vier Jahreszeiten jeweils über 94 % lag, was die Zuverlässigkeit und Wissenschaftlichkeit des Datensatzes gewährleistet. Dieser Datensatz zeichnet sich durch multiphasische Eigenschaften und hohe Annotationsgenauigkeit aus und bietet wichtige Datenunterstützung für die Kartierung dynamischer Gewässer und die Überwachung dynamischer Veränderungen in hochauflösenden Fernerkundungsbildern. Zur Überprüfung der Praxistauglichkeit wurden Experimente mit verschiedenen Methoden zur Extraktion von Gewässern durchgeführt, darunter Schwellenwertsegmentierung, traditionelle maschinelle Lernalgorithmen und Deep Learning. Die Ergebnisse zeigen, dass dieser Datensatz das Training und die Bewertung verschiedener Methoden effektiv unterstützt und eine zuverlässige Datenbasis zur Leistungsverbesserung dynamischer Gewässerextraktionsalgorithmen bietet.

Schlüsselwort

Gaofen-1; dynamische Gewässer; Gewässerextraktion; Datensatz; Merkmalsextraktion

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