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home/Knowledge Base/CODES/Spectral Unmixing/Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing

Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing

April 12, 2021

Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing
J. Yao, D. Hong, L. Xu, D. Meng, J. Chanussot and Z. Xu

IEEE Transactions on Geoscience and Remote Sensing
DOI: 10.1109/TGRS.2021.3069845

Tags:hyperspectraltensorunmixing
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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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    • Hyperspectral Super Resolution
    • Infrared
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    • Multi-modal
    • Object Detection
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    • Sequences
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    • Super Resolution
    • Synthetic Aperture Radar and Radar Sounder
    • Target Detection
    • Tensor
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  Spectral Superresolution of Multispectral Imagery With Joint Sparse and Low-Rank Learning

SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification  

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