Diffusion-Model-Based Hyperspectral Unmixing Using Spectral Prior Distribution-Reference-Cited by-同舟云学术

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Diffusion-Model-Based Hyperspectral Unmixing Using Spectral Prior Distribution

Published:2024 Issue: Volume:62 Page:1-16
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Deng Keli¹^ORCID,Qian Yuntao¹^ORCID,Nie Jie²^ORCID,Zhou Jun³^ORCID

Affiliation:

1. Institute of Artificial Intelligence, College of Computer Science, Zhejiang University, Hangzhou, China

2. Department of Computer Science and Technology, Ocean University of China, Qingdao, China

3. School of Information and Communication Technology, Griffith University, Nathan, QLD, Australia

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10545540.pdf?arnumber=10545540

Reference52 articles.

1. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

2. Collaborative Sparse Hyperspectral Unmixing Using $l_{0}$ Norm

3. A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents

4. Constrained subpixel target detection for remotely sensed imagery

5. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery

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. DEMAE: Diffusion-Enhanced Masked Autoencoder for Hyperspectral Image Classification With Few Labeled Samples;IEEE Transactions on Geoscience and Remote Sensing;2024

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