Non-Negative Matrix Factorization Based on Smoothing and Sparse Constraints for Hyperspectral Unmixing

Non-Negative Matrix Factorization Based on Smoothing and Sparse Constraints for Hyperspectral Unmixing

Hyperspectral unmixing (HU) is a technique for estimating a set of pure source signals (end members) and their proportions (abundances) from each pixel of the hyperspectral image. Non-negative matrix factorization (NMF) can decompose the observation matrix into the product of two non-negative matric...

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Bibliographic Details
Main Authors: Xiangxiang Jia, Baofeng Guo
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Sensors
Subjects:
hyperspectral unmixing
non-negative matrix factorization
reweighted sparseness
total variation
piecewise smoothness constraint
Online Access:https://www.mdpi.com/1424-8220/22/14/5417

Internet

https://www.mdpi.com/1424-8220/22/14/5417

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