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home/Knowledge Base/CODES/Multi-modal/Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks,

Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks,

November 14, 2023

Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks
Danfeng Hong, Bing Zhang, Hao Li, Yuxuan Li, Jing Yao, Chenyu Li, Martin Werner, Jocelyn Chanussot, Alexander Zipf and Xiao Xiang Zhu

Remote Sensing of Environment,
Volume 299, 2023, 113856,
ISSN 0034-4257,
https://doi.org/10.1016/j.rse.2023.113856.

 

corresponding code and data are available on GitHub:
https://github.com/danfenghong/RSE_Cross-city

 

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Categories
  • CODES
    • Anomaly
    • Change Detection
    • Classification of Hyperspectral Images
    • Classification of Remote Sensing Data
    • Data fusion: hyperspectral + Lidar
    • Data fusion: Hyperspectral + Multispectral
    • Deep Learning
    • Denoising
    • Feature Extraction
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    • Graphs, Manifold
    • Hyperspectral
    • Hyperspectral remote sensing
    • Hyperspectral Super Resolution
    • Infrared
    • Machine Learning in Remote Sensing
    • Multi-modal
    • Object Detection
    • Pansharpening
    • Registration
    • Sequences
    • Spectral Unmixing
    • Super Resolution
    • Synthetic Aperture Radar and Radar Sounder
    • Target Detection
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    • Transformer
  • DATA

  Geometric Multimodal Learning Based on Local Signal Expansion for Joint Diagonalization

CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences  

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