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home/Knowledge Base/CODES/Multi-modal/CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences

CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences

May 5, 2020

CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences
Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu

IEEE Transactions on Geoscience and Remote Sensing (TGRS)
Volume:    57    , Issue: 7
Publication Year: 2019 , Page(s): 4349 – 4359

code link: https://drive.google.com/open?id=1ErfS0-4-wXYjOPQAv3w5c7b3AW9vHKHD

 

Tags:Common subspace learning (CoSpace)cross-modality learninghyperspectrallandcover classificationmulti-spectral (MS)remote sensing
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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
    • Graphs
    • 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
    • Tensor
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  DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral Imagery

Class-Specific Sparse Multiple Kernel Learning for Spectral–Spatial Hyperspectral Image Classification  

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