A Deep Disentangled Approach for Interpretable Hyperspectral Unmixing
Author:
Affiliation:
1. Université de Lorraine,CNRS, CRAN, Vandoeuvre-lès-Nancy,France
2. Northeastern University,Dept. of Electrical & Computer Engineering,Boston,MA,USA
Funder
National Geographic Society
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10095764.pdf?arnumber=10095764
Reference27 articles.
1. Probabilistic generative model for hyperspectral unmixing accounting for endmember variability;shi;IEEE Transactions on Geoscience and Remote Sensing,2021
2. Nonlinear Unmixing of Hyperspectral Data via Deep Autoencoder Networks
3. Hyperspectral Unmixing With Spectral Variability Using a Perturbed Linear Mixing Model
4. Learning disentangled representations with semi-supervised deep generative models;siddharth;Advances in neural information processing systems,2017
5. Hyperspectral Unmixing Using a Neural Network Autoencoder
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing;IEEE Transactions on Computational Imaging;2023
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