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home/Knowledge Base/CODES/Spectral Unmixing/CyCU-Net: Cycle-Consistency Unmixing Network by Learning Cascaded Autoencoders

CyCU-Net: Cycle-Consistency Unmixing Network by Learning Cascaded Autoencoders

April 6, 2021

CyCU-Net: Cycle-Consistency Unmixing Network by Learning Cascaded Autoencoders
L. Gao, Z. Han, D. Hong, B. Zhang and J. Chanussot,

IEEE Transactions on Geoscience and Remote Sensing
DOI: 10.1109/TGRS.2021.3064958

Tags:cascaded autoencoderscycle consistencydeep learninghyperspectral unmixingremote sensingself-perception
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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
    • Transformer
  • DATA

  Deep Encoder–Decoder Networks for Classification of Hyperspectral and LiDAR Data

Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution  

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Mail : Jocelyn Chanussot

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http://www.jocelyn-chanussot.net

 

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