Früher|Nächster

Großflächige Landbedeckungskartierung basierend auf multimodalen Daten und multimodaler Modellfusion

  • role： First author第一作者
  • Affiliation：

    Hangzhou Institute for Advanced Study, School of Physics and Optoelectronic Engineering, University of Chinese Academy of Sciences, Hangzhou 310024, China

    Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China

  • Email：ruyifan22@mails.ucas.ac.cn
  • Introduction：茹逸凡，研究方向为机器学习与语义分割、遥感土地覆盖制图、图像处理、多光谱与高光谱。E-mail： ruyifan22@mails.ucas.ac.cn
RU Yifan12,  
  • Affiliation：

    Hangzhou Institute for Advanced Study, School of Physics and Optoelectronic Engineering, University of Chinese Academy of Sciences, Hangzhou 310024, China

TANG Guoliang1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China

  • Email：Lichunlai@mail.sitp.ac.cn
  • Introduction：李春来，研究方向为光谱成像技术、数字图形处理、微弱信号处理技术。E-mail：Lichunlai@mail.sitp.ac.cn
LI Chunlai2*

Resümee

Die aktuelle Landbedeckungskartierung steht vor zentralen Herausforderungen wie der Begrenzung auf einzelne Datenquellen, hoher Datenheterogenität und unzureichender Generalisierungsfähigkeit einzelner Modelle. Daher basiert diese Studie auf den spektralen Merkmalen multispektraler Bilder MSI (Multispectral Imagery) und den unterschiedlichen Eigenschaften der Synthetic Aperture Radar (SAR)-Daten, um einen multimodalen (MSI+SAR) Datensatz zu erstellen, der die Nachteile unimodaler SAR-Daten in komplexen Szenarien ausgleicht. Darüber hinaus wurde nach systematischer Bewertung von sieben repräsentativen Modellen auf Modellebene eine multimodale Verschmelzungsmethode vorgeschlagen, die drei Architekturtypen integriert: FCN repräsentiert Convolutional Neural Networks (CNN), ConViT repräsentiert Visual Transformers (ViT) und CoAtNet repräsentiert eine hybride CNN-ViT-Architektur. Die experimentellen Ergebnisse zeigen, dass multimodale (MSI+SAR) Daten im Vergleich zu unimodalen SAR-Daten eine signifikante Verbesserung bei der Gesamtgenauigkeit OA (Overall Accuracy) und dem frequenzgewichteten Intersection over Union FWIoU (Frequency-Weighted Intersection over Union) erzielen, zwei repräsentative semantische Segmentierungsmetriken. Der vorgeschlagene multimodale Verschmelzungsrahmen verbessert zudem beide Indikatoren weiter. Die in dieser Studie vorgeschlagene innovative Methode stärkt nicht nur die Fähigkeit der Modelle, komplexe Geländecharakteristika zu extrahieren, sondern verbessert auch effektiv die Klassifizierung in der großflächigen Landbedeckungskartierung und bestätigt das große Potenzial multimodaler Daten und multimodaler Modellfusion in der Fernerkundung.

Schlüsselwort

Landbedeckungskartierung;multispektral;Synthetic Aperture Radar;multimodal;multimodale Modellfusion;Convolutional Neural Networks;Visual Transformers

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