Joint Bayesian Hyperspectral Unmixing For Change Detection

Spectral unmixing allows to extract endmembers and estimate their proportions in hyperspectral data. Each observed pixel is considered to be a linear combination of several endmembers spectra. Based on a novel hierarchical Bayesian model, change detection into hyperspectral images is achieved by unm...

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Bibliographic Details
Published in2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS) pp. 37 - 40
Main Authors Gharbi, W., Chaari, L., Benazza-Benyahia, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2020
Subjects
Bayes methods
Bayesian methods
Change Detection
Complexity theory
Data mining
Geoscience and remote sensing
Hyperspectral imaging
Linear unmixing
MCMC methods
Spatial resolution
Superresolution
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Summary:Spectral unmixing allows to extract endmembers and estimate their proportions in hyperspectral data. Each observed pixel is considered to be a linear combination of several endmembers spectra. Based on a novel hierarchical Bayesian model, change detection into hyperspectral images is achieved by unmixing. A Gibbs sampler is proposed to overcome the complexity of integrating the resulting posterior distribution. The performance of the proposed Bayesian change detection method is evaluated on real data. It provides binary detection with a precision rate up to 98.90%.
DOI:10.1109/M2GARSS47143.2020.9105275