Matrix Cofactorization for Joint Unmixing and Classification of Hyperspectral Images. Lagrange, A., Fauvel, M., May, S., Bioucas-Dias, J. M., & Dobigeon, N. In 2019 27th European Signal Processing Conference (EUSIPCO), pages 01-05, Sep., 2019. ![Matrix Cofactorization for Joint Unmixing and Classification of Hyperspectral Images [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex This paper introduces a matrix cofactorization approach to perform spectral unmixing and classification jointly. After formulating the unmixing and classification tasks as matrix factorization problems, a link is introduced between the two coding matrices, namely the abundance matrix and the feature matrix. This coupling term can be interpreted as a clustering term where the abundance vectors are clustered and the resulting attribution vectors are then used as feature vectors. The overall non-smooth, non-convex optimization problem is solved using a proximal alternating linearized minimization algorithm (PALM) ensuring convergence to a critical point. The quality of the obtained results is finally assessed by comparison to other conventional algorithms on semi-synthetic yet realistic dataset.
@InProceedings{8903037,
author = {A. Lagrange and M. Fauvel and S. May and J. M. Bioucas-Dias and N. Dobigeon},
booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
title = {Matrix Cofactorization for Joint Unmixing and Classification of Hyperspectral Images},
year = {2019},
pages = {01-05},
abstract = {This paper introduces a matrix cofactorization approach to perform spectral unmixing and classification jointly. After formulating the unmixing and classification tasks as matrix factorization problems, a link is introduced between the two coding matrices, namely the abundance matrix and the feature matrix. This coupling term can be interpreted as a clustering term where the abundance vectors are clustered and the resulting attribution vectors are then used as feature vectors. The overall non-smooth, non-convex optimization problem is solved using a proximal alternating linearized minimization algorithm (PALM) ensuring convergence to a critical point. The quality of the obtained results is finally assessed by comparison to other conventional algorithms on semi-synthetic yet realistic dataset.},
keywords = {convergence of numerical methods;convex programming;feature extraction;image classification;iterative methods;least squares approximations;matrix decomposition;minimisation;joint unmixing;hyperspectral images;matrix cofactorization approach;unmixing classification;matrix factorization problems;abundance matrix;feature matrix;coupling term;clustering term;abundance vectors;feature vectors;nonconvex optimization problem;attribution vectors;proximal alternating linearized minimization algorithm;PALM algorithm;Optimization;Couplings;Task analysis;Indexes;Hyperspectral imaging;Clustering algorithms;supervised learning;spectral unmixing;cofactorization;hyperspectral images},
doi = {10.23919/EUSIPCO.2019.8903037},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570531255.pdf},
}