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- Title
A STOCHASTIC PROXIMAL GRADIENT METHOD FOR LINEAR HYPERSPECTRAL UNMIXING.
- Authors
XIUMING LI; FANGFANG XU; YU-HONG DAI
- Abstract
Linear hyperspectral unmixing (LHU) aims to extract endmembers from mixed pixels in hyperspectral images, which is also a large-scale problem as the number of pixels in hyperspectral images is very large. In this paper, by virtue of the Moreau envelope, we formulate the LHU as an unconstrained optimization problem. Then we adopt the proximal gradient descent method to solve the model, and consider a stochastic version of the method which is for dealing with large-scale scenario. Numerical results are provided to demonstrate the simplicity and efficiency of our proposed model.
- Subjects
SPECTRAL imaging; SIMPLICITY; PIXELS
- Publication
Journal of Applied & Numerical Optimization, 2024, Vol 6, Issue 3, p323
- ISSN
2562-5527
- Publication type
Article
- DOI
10.23952/jano.6.2024.3.02