Probabilistic Generative Model for Hyperspectral Unmixing Accounting for Endmember Variability

Shuaikai Shi, Min Zhao, Lijun Zhang, Yoann Altmann, Jie Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


The complex nature of hyperspectral images makes the analysis of spectral signatures a challenging task in remote sensing. For quantitative analysis, spectral unmixing is a well-established and effective tool to analyze the spectra and spatial distribution of substances in the scene. The classical unmixing algorithms usually fail to tackle spectral variability caused by variations in environmental conditions. Many variants based on the linear mixing process have been proposed to tackle this problem; however, the spectral variability modeling capacity of these algorithms is usually insufficient. In this article, we present a probabilistic generative model to address endmember variability and provide more accurate abundance and endmember estimates. The proposed model simultaneously extracts the endmembers and estimates abundances in an unsupervised manner. In particular, it allows fitting arbitrary endmember distributions through the nonlinear modeling capability of neural networks compared to other methods that use parametric endmember variability models. The performance of the proposed approach is evaluated on both synthetic and real datasets. Experimental results show its superiority in comparison with other state-of-the-art methods. The code of this work is available at for the sake of reproducibility.

Original languageEnglish
Article number5516915
JournalIEEE Transactions on Geoscience and Remote Sensing
Early online date21 Oct 2021
Publication statusPublished - 11 Feb 2022


  • Endmember variability
  • Hyperspectral unmixing
  • Probabilistic generative model
  • Variational inference (VI)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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