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EllipticNet: Erkennung von gerichteten Objekten auf Basis der elliptischen Gleichung

  • role： First author第一作者
  • Affiliation：

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

  • Email：tukelong@cug.edu.cn
  • Introduction：涂可龙，研究方向为高分遥感影像解译。E-mail： tukelong@cug.edu.cn
TU Kelong1,  
  • Affiliation：

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

QING Yaxian2,  
  • Affiliation：

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

LI Zhenqiang3,  
  • Affiliation：

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

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

YANG Chao13,  
  • role： Corresponding author通信作者
  • Affiliation：

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

    National Engineering Research Center of Geographic Information System, 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 Kunlun13*,  
  • Affiliation：

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

WU Huayi2

Resümee

Die Erkennung von gerichteten Objekten mit Fernerkundungsbildern ist eine herausfordernde Aufgabe im Bereich der Computer Vision, da herkömmliche Methoden des horizontalen Rahmens keine präzise Lokalisierung von Objekten verschiedener Größen, willkürlicher Ausrichtung und dichter Anordnung ermöglichen. Die derzeit weit verbreitete fiv-parametergerichtete Rahmendarstellung, aufgrund der Periodizität des Richtungswinkels und des Problems des Grenztauschs, erhöht die Komplexität des Modelltrainings. Um die oben genannten Probleme zu lösen, wird in diesem Artikel ein Modell zur Erkennung von gerichteten Objekten auf der Basis der elliptischen Gleichung EllipticNet (Netzwerk zur Erkennung von gerichteten Objekten auf Basis der elliptischen Gleichung) vorgestellt. Erstens zerlegt EllipticNet das Problem der Winkelvorhersage in zwei Teilprobleme: quantitative Winkelregression und Richtungsklassifizierung, wodurch das Problem der Diskontinuität der Grenzen des fünfparametrigen gerichteten Rahmens überwunden wird. Darüber hinaus wird in diesem Artikel eine an die Ellipse gebundene Verlustfunktion vorgeschlagen, die die internen geometrischen Beziehungen zwischen den Ellipsenparametern verbessert und die Robustheit des EllipticNet-Trainings erhöht. Darüber hinaus wird in diesem Artikel ein Schichtenweise hohler Raumfaltungspyramidenmodul vorgestellt, das die Fähigkeit von EllipticNet zur Darstellung von Mehrskalenmerkmalen signifikant verbessert. Schließlich zeigen vergleichende Experimente an bekannten Fernerkundungsdatensätzen wie DOTA, HRSC2016 und UCAS_AOD, dass der in diesem Artikel vorgestellte Ansatz bezüglich Leistung und Effizienz wettbewerbsfähig ist und somit den praktischen Wert dieses Ansatzes bei der Erkennung von gerichteten Objekten in Fernerkundungsbildern untermauert.

Schlüsselwort

Erkennung von gerichteten Objekten, elliptische Gleichung, Merkmalsverbesserung, hochauflösende Fernerkundungsbilder

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