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Joint Bayesian Hyperspectral Unmixing for change detection

Gharbi, Walma and Chaari, Lotfi and Benazza-Benyahia, Amel Joint Bayesian Hyperspectral Unmixing for change detection. (2020) In: Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS 2020), 9 March 2020 - 11 March 2020 (Tunis, Tunisia).

(Document in English)

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Official URL: https://doi.org/10.1109/M2GARSS47143.2020.9105275


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%.

Item Type:Conference or Workshop Item (Paper)
HAL Id:hal-02942312
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Digital Research Center of Sfax (TUNISIA)
Other partners > Université de Carthage (TUNISIA)
Laboratory name:
Tunisian program of Ministry of Higher Education and Scientific Research (Tunisia)
Deposited On:04 Sep 2020 13:40

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