![](/images/graphics-bg.png)
Active Set Type Algorithms for Nonnegative Matrix Factorization in Hyperspectral Unmixing
Joint Authors
Han, Congying
Sun, Li
Liu, Ziwen
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-21
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Hyperspectral unmixing is a powerful method of the remote sensing image mining that identifies the constituent materials and estimates the corresponding fractions from the mixture.
We consider the application of nonnegative matrix factorization (NMF) for the mining and analysis of spectral data.
In this paper, we develop two effective active set type NMF algorithms for hyperspectral unmixing.
Because the factor matrices used in unmixing have sparse features, the active set strategy helps reduce the computational cost.
These active set type algorithms for NMF is based on an alternating nonnegative constrained least squares (ANLS) and achieve a quadratic convergence rate under the reasonable assumptions.
Finally, numerical tests demonstrate that these algorithms work well and that the function values decrease faster than those obtained with other algorithms.
American Psychological Association (APA)
Sun, Li& Han, Congying& Liu, Ziwen. 2019. Active Set Type Algorithms for Nonnegative Matrix Factorization in Hyperspectral Unmixing. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1200726
Modern Language Association (MLA)
Sun, Li…[et al.]. Active Set Type Algorithms for Nonnegative Matrix Factorization in Hyperspectral Unmixing. Mathematical Problems in Engineering No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1200726
American Medical Association (AMA)
Sun, Li& Han, Congying& Liu, Ziwen. Active Set Type Algorithms for Nonnegative Matrix Factorization in Hyperspectral Unmixing. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1200726
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1200726