Spectral Variability Bayesian Unmixing for Hyperspectral Sequence in Wavelet Domain-Reference-Cited by-同舟云学术

Spectral Variability Bayesian Unmixing for Hyperspectral Sequence in Wavelet Domain

Published:2023 Issue: Volume:61 Page:1-19
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Liu Hongyi¹^ORCID,Lu Youkang¹,Wu Zebin²^ORCID,Du Qian³^ORCID,Chanussot Jocelyn⁴^ORCID,Wei Zhihui²^ORCID

Affiliation:

1. School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing, China

2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

3. Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USA

4. Centre National de la Recherche Scientifique (CNRS), Grenoble Institute of Technology (Grenoble INP), GIPSA-Lab, Université Grenoble Alpes, Grenoble, France

Funder

National Natural Science Foundation of China

Jiangsu Provincial Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10237220.pdf?arnumber=10237220

Reference58 articles.

1. Bayesian Estimation of Linear Mixtures Using the Normal Compositional Model. Application to Hyperspectral Imagery

2. Unmixing urban hyperspectral imagery using probability distributions to represent endmember variability

3. A spectral unmixing method based on wavelet weighted similarity

4. Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing

5. Vertex component analysis: a fast algorithm to unmix hyperspectral data

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