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Hyperspectral Unmixing via L 1/2 Sparsity-Constrained Nonnegative Matrix Factorization

Date

Authors

Qian, Yuntao

Jia, Sen

Zhou, Jun

Robles-Kelly, Antonio

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

Abstract

Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the

Keywords

Keywords: Additivity; Endmembers; Hyperspectral Data; Hyperspectral imagery; Hyperspectral unmixing; Iterative estimation; L 1/2 regularizer; Material classification; Nonnegative matrix factorization; nonnegative matrix factorization (NMF); Pre-processing Hyperspectral unmixing; L1/2 regularizer; nonnegative matrix factorization (NMF); sparse coding

URI

http://hdl.handle.net/1885/64585

Collections

ANU Research Publications

Source

IEEE Transactions on Geoscience and Remote Sensing

Type

Journal article

DOI

10.1109/TGRS.2011.2144605

Restricted until

2037-12-31

Downloads

File

Description

01_Qian_Hyperspectral_Unmixing_via_L_2011.pdf (1.81 MB)

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