Unmixing multitemporal hyperspectral images accounting for smooth and abrupt variations. Thouvenin, P., Dobigeon, N., & Tourneret, J. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 2378-2382, Aug, 2017.
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Paper doi abstract bibtex
Paper doi abstract bibtex A classical problem in hyperspectral imaging, referred to as hyperspectral unmixing, consists in estimating spectra associated with each material present in an image and their proportions in each pixel. In practice, illumination variations (e.g., due to declivity or complex interactions with the observed materials) and the possible presence of outliers can result in significant changes in both the shape and the amplitude of the measurements, thus modifying the extracted signatures. In this context, sequences of hyperspectral images are expected to be simultaneously affected by such phenomena when acquired on the same area at different time instants. Thus, we propose a hierarchical Bayesian model to simultaneously account for smooth and abrupt spectral variations affecting a set of multitemporal hyperspectral images to be jointly unmixed. This model assumes that smooth variations can be interpreted as the result of endmember variability, whereas abrupt variations are due to significant changes in the imaged scene (e.g., presence of outliers, additional endmembers, etc.). The parameters of this Bayesian model are estimated using samples generated by a Gibbs sampler according to its posterior. Performance assessment is conducted on synthetic data in comparison with state-of-the-art unmixing methods.
@InProceedings{8081636,
author = {P. Thouvenin and N. Dobigeon and J. Tourneret},
booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
title = {Unmixing multitemporal hyperspectral images accounting for smooth and abrupt variations},
year = {2017},
pages = {2378-2382},
abstract = {A classical problem in hyperspectral imaging, referred to as hyperspectral unmixing, consists in estimating spectra associated with each material present in an image and their proportions in each pixel. In practice, illumination variations (e.g., due to declivity or complex interactions with the observed materials) and the possible presence of outliers can result in significant changes in both the shape and the amplitude of the measurements, thus modifying the extracted signatures. In this context, sequences of hyperspectral images are expected to be simultaneously affected by such phenomena when acquired on the same area at different time instants. Thus, we propose a hierarchical Bayesian model to simultaneously account for smooth and abrupt spectral variations affecting a set of multitemporal hyperspectral images to be jointly unmixed. This model assumes that smooth variations can be interpreted as the result of endmember variability, whereas abrupt variations are due to significant changes in the imaged scene (e.g., presence of outliers, additional endmembers, etc.). The parameters of this Bayesian model are estimated using samples generated by a Gibbs sampler according to its posterior. Performance assessment is conducted on synthetic data in comparison with state-of-the-art unmixing methods.},
keywords = {Bayes methods;geophysical image processing;geophysical techniques;hyperspectral imaging;object detection;remote sensing;spectral analysis;unmixing multitemporal hyperspectral images;abrupt variations;classical problem;hyperspectral imaging;illumination variations;observed materials;hierarchical Bayesian model;smooth variations;abrupt spectral variations;imaged scene;unmixing methods;Gibbs sampler;synthetic data;Bayes methods;Hyperspectral imaging;Europe;Signal processing;Redundancy;Additives;Gaussian distribution},
doi = {10.23919/EUSIPCO.2017.8081636},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570341763.pdf},
}Downloads: 0
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