Bodensedimentklassifikation von airborne LiDAR-Tiefenpunkten unter Berücksichtigung der Merkmalsauswahl

  • role: First author第一作者
  • Affiliation:

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

    Key Laboratory of Ocean Geomatics, Ministry of Natural Resources of China, Qingdao 266590, China

    Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

  • Email:sudianpeng@126.com
  • Introduction:宿殿LiDARE-mail sudianpeng@126.com
SU Dianpeng123,  
  • Affiliation:

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

HUANG Yu1,  
  • role: Corresponding author通信作者
  • Affiliation:

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

    Key Laboratory of Ocean Geomatics, Ministry of Natural Resources of China, Qingdao 266590, China

  • Email:flyang@126.com
  • Introduction: E-mail flyang@126.com
YANG Fanlin12*,  
  • Affiliation:

    Key Laboratory of Ocean Geomatics, Ministry of Natural Resources of China, Qingdao 266590, China

    Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

ZHAO Dineng23,  
  • Affiliation:

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

    Key Laboratory of Ocean Geomatics, Ministry of Natural Resources of China, Qingdao 266590, China

YANG Anxiu12,  
  • Affiliation:

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

LIU Jiaoyang1

Resümee

Die Bodensedimentklassifikation basierend auf der airborne LiDAR-Bathymetrie (ALB)-Technologie kann grundlegende Daten für die Erschließung mariner Ressourcen in flachen Meeresgebieten, den Meeresschutz und den maritimen Ingenieurbau liefern und ist von großer Bedeutung für maritime Aktivitäten und die Meereswissenschaft. Um das Problem der Merkmalsredundanz bei der ALB-Bodensedimentklassifikation zu lösen, schlägt dieser Artikel einen Bodensedimentklassifikationsalgorithmus vor, der eine bevorzugte Auswahl von Wellenform- und Geländemerkmalen berücksichtigt. Basierend auf der Extraktion von Wellenform- und Geländemerkmalen wurde ein Relief-F-Merkmalsauswahlmodell entwickelt, das durch Berechnung des Beitrags jedes Merkmals zur Sedimentklassifikation eine multivariate Merkmalsauswahl ermöglicht; anschließend werden mit drei überwachten Klassifikatoren, Random Forest (RF), Support Vector Machine (SVM) und Back Propagation Neural Network (BPNN), fünf Sedimenttypen – Korallenriffe, Kies, Sand, Vegetation und Küstenzone – klassifiziert. Zur Validierung der vorgeschlagenen Klassifikationsmethode wurden Experimente mit gemessenen ALB-Daten von der Ganquan-Insel im Xisha-Archipel durchgeführt. Die Ergebnisse zeigen, dass nach der Merkmalsauswahl mit dem Relief-F-Algorithmus die Klassifikationsgenauigkeit von RF, SVM und BPNN um 1,1 %, 1,1 % bzw. 2,7 % verbessert wurde; dabei weist die Random-Forest-Klassifikation die höchste Genauigkeit auf, mit einer Gesamtgenauigkeit (OA) von 95,36 % und einem Kappa-Koeffizienten von 0,94. Die Forschungsergebnisse können eine effektive technische Unterstützung für die Anforderungen der Bodensedimentklassifikation in Bereichen wie dem maritimen Ingenieurbau bieten.

Schlüsselwort

Airborne LiDAR-Tiefenmessung; Bodensedimentklassifikation; Wellenformmerkmale; Geländemerkmale; Relief-F-Merkmalsauswahlmodell; Bildverarbeitung; Meer

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