Spectral Variability Augmented Two-Stream Network for Hyperspectral Sparse Unmixing
Author:
Affiliation:
1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China
2. Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USA
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology
Link
http://xplorestaging.ieee.org/ielx7/8859/9651998/09919800.pdf?arnumber=9919800
Cited by 9 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. Superpixel-Based Low-Rank Tensor Factorization for Blind Nonlinear Hyperspectral Unmixing;IEEE Sensors Journal;2024-04-15
2. A Reversible Generative Network for Hyperspectral Unmixing With Spectral Variability;IEEE Transactions on Geoscience and Remote Sensing;2024
3. Blind Unmixing Using Dispersion Model-Based Autoencoder to Address Spectral Variability;IEEE Transactions on Geoscience and Remote Sensing;2024
4. Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package;IEEE Transactions on Geoscience and Remote Sensing;2024
5. A blind convolutional deep autoencoder for spectral unmixing of hyperspectral images over waterbodies;Frontiers in Earth Science;2023-10-13
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