@InProceedings{8902707,
  author = {F. Z. Benhalouche and M. S. Karoui and Y. Deville},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {An NMF-Based Approach for Hyperspectral Unmixing Using a New Multiplicative-tuning Linear Mixing Model to Address Spectral Variability},
  year = {2019},
  pages = {1-5},
  abstract = {In this work, a new approach is presented for unmixing remote sensing hyperspectral data. This approach considers a linear mixing model that is introduced in these investigations to handle the spectral variability phenomenon, which is usually observed in the considered data and which is here modeled in a multiplicative form. The proposed algorithm, which is based on a pixel-by-pixel non-negative matrix factorization method, uses multiplicative update rules for minimizing a cost function that takes into account the introduced linear mixing model. Tests, by means of realistic synthetic data, are conducted to evaluate the performance of the proposed approach, and the obtained results are compared to those of methods from the literature. These test results show that the proposed approach outperforms all other tested methods.},
  keywords = {geophysical image processing;hyperspectral imaging;remote sensing;NMF-based approach;hyperspectral unmixing;spectral variability;unmixing remote sensing hyperspectral data;multiplicative form;pixel-by-pixel nonnegative matrix factorization method;multiplicative update rules;realistic synthetic data;multiplicative-tuning linear mixing model;Data models;Hyperspectral imaging;Cost function;Reflectivity;Standards;Hyperspectral imaging;linear spectral unmixing;spectral variability;non-negative matrix factorization;multiplicative update rules},
  doi = {10.23919/EUSIPCO.2019.8902707},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570533687.pdf},
}

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This approach considers a linear mixing model that is introduced in these investigations to handle the spectral variability phenomenon, which is usually observed in the considered data and which is here modeled in a multiplicative form. The proposed algorithm, which is based on a pixel-by-pixel non-negative matrix factorization method, uses multiplicative update rules for minimizing a cost function that takes into account the introduced linear mixing model. Tests, by means of realistic synthetic data, are conducted to evaluate the performance of the proposed approach, and the obtained results are compared to those of methods from the literature. 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Deville},\n booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},\n title = {An NMF-Based Approach for Hyperspectral Unmixing Using a New Multiplicative-tuning Linear Mixing Model to Address Spectral Variability},\n year = {2019},\n pages = {1-5},\n abstract = {In this work, a new approach is presented for unmixing remote sensing hyperspectral data. This approach considers a linear mixing model that is introduced in these investigations to handle the spectral variability phenomenon, which is usually observed in the considered data and which is here modeled in a multiplicative form. The proposed algorithm, which is based on a pixel-by-pixel non-negative matrix factorization method, uses multiplicative update rules for minimizing a cost function that takes into account the introduced linear mixing model. 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