Minimum Volume Simplex Analysis: A Fast Algorithm for Linear Hyperspectral Unmixing-Reference-Cited by-同舟云学术

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Minimum Volume Simplex Analysis: A Fast Algorithm for Linear Hyperspectral Unmixing

Published:2015-09 Issue:9 Volume:53 Page:5067-5082
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Jun Li ,Agathos Alexander,Zaharie Daniela,Bioucas-Dias Jose M.,Plaza Antonio,Xia Li

Funder

Portuguese Science and Technology Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/7119654/07090960.pdf?arnumber=7090960

Cited by 173 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. Tucker tensor decomposition with rank estimation for sparse hyperspectral unmixing;International Journal of Remote Sensing;2024-06-16

2. Temperature scaling unmixing framework based on convolutional autoencoder;International Journal of Applied Earth Observation and Geoinformation;2024-05

3. A new convex model for linear hyperspectral unmixing;Journal of Computational and Applied Mathematics;2024-05

4. CDCTA: cascaded dual-constrained transformer autoencoder for hyperspectral unmixing with endmember variability and spectral geometry;Journal of Applied Remote Sensing;2024-04-18

5. Superpixel-Based Low-Rank Tensor Factorization for Blind Nonlinear Hyperspectral Unmixing;IEEE Sensors Journal;2024-04-15

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