• Home
  • CODES
    • Classification of Hyperspectral Images
    • Classification of Remote Sensing Data
    • Data fusion: hyperspectral + Lidar
    • Hyperspectral Super Resolution
    • Machine Learning in Remote Sensing
    • Pansharpening
    • Registration
    • Spectral Unmixing
  • DATA
  • About us
  • Home
  • CODES
    • Classification of Hyperspectral Images
    • Classification of Remote Sensing Data
    • Data fusion: hyperspectral + Lidar
    • Hyperspectral Super Resolution
    • Machine Learning in Remote Sensing
    • Pansharpening
    • Registration
    • Spectral Unmixing
  • DATA
  • About us
home/Knowledge Base/CODES/Spectral Unmixing/Empirical Automatic Estimation of the Number of Endmembers in Hyperspectral Images

Empirical Automatic Estimation of the Number of Endmembers in Hyperspectral Images

April 23, 2018

Empirical Automatic Estimation of the Number of Endmembers in Hyperspectral Images
Bin Luo; Jocelyn Chanussot; Sylvain Doute; Liangpei Zhang

IEEE Geoscience and Remote Sensing Letters
Year: 2013, Volume: 10, Issue: 1
Pages: 24 – 28

 

 

Tags:imagingspectral analysis
Attached Files
#
File Type
File Size
Download
1 .pdf 556.68 KB Empirical automatic estimation of the number of endmembers in hyperspectral images
2 .zip 1.47 KB det_number.m
3 .rar 988.00 B ELM
Related Articles
  • Multimodal Hyperspectral Unmixing: Insights From Attention Networks
  • Hyperspectral endmember extraction using convex geometry
  • Multimodal Hyperspectral Unmixing: Insights from Attention Networks
  • MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing
  • Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing
  • Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing

Didn't find your answer? Contact Us

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
  • DATA

  Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing

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

Contact

Mail : Jocelyn Chanussot

Like Us On Facebook
Facebook Pagelike Widget
Follow Us On Twitter
Follow @RemoteOpen
Links

http://www.jocelyn-chanussot.net