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home/Knowledge Base/CODES/Hyperspectral/Revisiting Deep Hyperspectral Feature Extraction Networks via Gradient Centralized Convolution

Revisiting Deep Hyperspectral Feature Extraction Networks via Gradient Centralized Convolution

November 24, 2021

Revisiting Deep Hyperspectral Feature Extraction Networks via Gradient Centralized Convolution
S. K. Roy, P. Kar, D. Hong, X. Wu, A. Plaza and J. Chanussot

IEEE Transactions on Geoscience and Remote Sensing
doi: 10.1109/TGRS.2021.3120198

code : https://github.com/swalpa/G2C-Conv3D-HSI

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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

  Semisupervised Cross-Scale Graph Prototypical Network for Hyperspectral Image Classification

Multimodal Hyperspectral Unmixing: Insights From Attention Networks  

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