Nonlinear unmixing of hyperspectral data with partially linear least-squares support vector regression

Nonlinear unmixing of hyperspectral data with partially linear least-squares support vector regression. Chen, J., Richard, C., Ferrari, A., & Honeine, P. In Proc. 38th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 2174 - 2178, Vancouver, Canada, May, 2013.

Link

Nonlinear unmixing of hyperspectral data with partially linear least-squares support vector regression [pdf]

Paper doi abstract bibtex

In recent years, nonlinear unmixing of hyperspectral data has become an attractive topic in hyperspectral image analysis, because nonlinear models appear as more appropriate to represent photon interactions in real scenes. For this challenging problem, nonlinear methods operating in reproducing kernel Hilbert spaces have shown particular advantages. In this paper, we derive an efficient nonlinear unmixing algorithm based on a recently proposed linear mixture/ nonlinear fluctuation model. A multi-kernel learning support vector regressor is established to determine material abundances and nonlinear fluctuations. Moreover, a low complexity locally-spatial regularizer is incorporated to enhance the unmixing performance. Experiments with synthetic and real data illustrate the effectiveness of the proposed method.

@INPROCEEDINGS{13.icassp.hype,
   author =  "Jie Chen and Cédric Richard and André Ferrari and Paul Honeine",
   title =  "Nonlinear unmixing of hyperspectral data with partially linear least-squares support vector regression",
   booktitle =  "Proc. 38th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
   address =  "Vancouver, Canada",
   month =  may,
   year  =  "2013",
   pages={2174 - 2178},
   doi={10.1109/ICASSP.2013.6638039}, 
   ISSN={1520-6149},
   keywords  =  "machine learning, hyperspectral",
   acronym =  "ICASSP",
   url_link= "https://ieeexplore.ieee.org/document/6638039",
   url_paper   =  "http://honeine.fr/paul/publi/13.icassp.hype.pdf",
   abstract={In recent years, nonlinear unmixing of hyperspectral data has become an attractive topic in hyperspectral image analysis, because nonlinear models appear as more appropriate to represent photon interactions in real scenes. For this challenging problem, nonlinear methods operating in reproducing kernel Hilbert spaces have shown particular advantages. In this paper, we derive an efficient nonlinear unmixing algorithm based on a recently proposed linear mixture/ nonlinear fluctuation model. A multi-kernel learning support vector regressor is established to determine material abundances and nonlinear fluctuations. Moreover, a low complexity locally-spatial regularizer is incorporated to enhance the unmixing performance. Experiments with synthetic and real data illustrate the effectiveness of the proposed method.}, 
   keywords={Hilbert spaces, hyperspectral imaging, image processing, least squares approximations, regression analysis, support vector machines, multikernel learning support vector regressor, nonlinear fluctuation model, linear mixture model, kernel Hilbert spaces, photon interactions, hyperspectral image analysis, partially linear least-squares support vector regression, hyperspectral data, nonlinear unmixing, Hyperspectral imaging, Kernel, Materials, Vectors, Support vector machines, Nonlinear unmixing, hyperspectral image, support vector regression, multi-kernel learning, spatial regularization}, 
   ISSN={1520-6149}, 
}

Downloads: 0