Mixing Support Detection-Based Alternating Direction Method of Multipliers for Sparse Hyperspectral Image Unmixing
Author:
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
Sichuan Science and Technology Program
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10851-024-01208-8.pdf
Reference46 articles.
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2. Bioucas-Dias, J., Figueiredo, M.: Alternating direction algorithms for constrained sparse regression: application to hyperspectral unmixing. In: Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, vol. 1, pp. 1–4 (2010)
3. Bioucas-Dias, J., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., Chanussot, J.: Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 5(2), 354–379 (2012)
4. Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)
5. Candès, E., Wakin, M., Boyd, S.: Enhancing sparsity by reweighted $$\ell _1$$ minimization. J. Fourier Anal. Appl. 14(5–6), 877–905 (2008)
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