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Erkennung von Bodenschattenobjekten in hochauflösenden Fernerkundungsbildern basierend auf Vorwissen über die Schattendirektion

  • role： First author第一作者
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China

  • Email：qikunlun@cug.edu.cn
  • Introduction：祁昆仑，研究方向为遥感影像解译和时空大数据。E-mail： qikunlun@cug.edu.cn
QI Kunlun1,  
  • Affiliation：

    China Mobile (Hangzhou) Information Technology Co., Ltd., Hangzhou 311100, China

MA Xinyue2,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China

JIN Zhun1,  
  • Affiliation：

    National Engineering Research Center of Geographic Information System, Wuhan 430074, China

QING Yaxian3,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

  • Email：lizhenqiang@cug.edu.cn
  • Introduction：李真强，研究方向为时空大数据挖掘。E-mail： lizhenqiang@cug.edu.cn
LI Zhenqiang4*,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

YANG Chao4,  
  • Affiliation：

    National Engineering Research Center of Geographic Information System, Wuhan 430074, China

WU Huayi3

Resümee

Die Beschriftung von Bodenschattenobjekten ist teuer und schwierig, um die reichhaltigen Informationen in hochauflösenden Fernerkundungsbildern umfassend zu erfassen. Der Mangel an Trainingsbeispielen schränkt die Leistung von überwachten Lernmodellen erheblich ein. Zur Lösung der oben genannten Probleme schlägt dieser Artikel eine Methode zur Erkennung von Bodenschattenobjekten in hochauflösenden Fernerkundungsbildern basierend auf Vorwissen über die Schattendirektion vor. Diese Methode untersucht die Wirksamkeit des Vorwissens über die Schattendirektion zur Darstellung fortgeschrittener semantischer Merkmale von Bodenschattenobjekten in Fernerkundungsbildern und konstruiert eine Hilfsaufgabe zur Erkennung von Bodenschattenobjekten in Fernerkundungsbildern basierend auf dieser Richtlinieninformation, um so eine selbstüberwachte Methode zur Erkennung von Bodenschattenobjekten zu realisieren. In diesem Artikel wurde ein Mechanismus zur Rauschverarbeitung unabhängig von Richtungsänderungen und eine Datenverstärkungsstrategie für die selbstüberwachte Schattenerkennung entwickelt, um die Fähigkeit tiefer neuronaler Netze zur Erfassung der Schlüsselmerkmale von Bodenschatten zu verbessern. Die experimentellen Ergebnisse auf dem AISD-Datensatz zeigen, dass diese Methode die Genauigkeit der Bodenschattenerkennung erheblich verbessert, und dass die Grenzen der Bodenschattenobjekte glatter und regelmäßiger sind und näher an der realen Geländesituation liegen, auch wenn nur eine geringe Anzahl von Etiketten verwendet wird.

Schlüsselwort

Schattenerkennung; Selbstüberwachtes Lernen; Datenaugmentierung; Vorwissen über die Schattendirektion; Hochauflösende Fernerkundungsbilder

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