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L1/2 Sparsity Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing

Date

Authors

Qian, Yuntao

Jia, Sen

Zhou, Jun

Robles-Kelly, Antonio

Publisher

IEEE Computer Society

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; Material classification; Nonnegative matrix factorization; Optimisations; Pre-processing step; Sparsity constraints; Algorithms; Factorization; Remote sensing; Matr

URI

http://hdl.handle.net/1885/62293

Collections

ANU Research Publications

Source

Proceedings of the Digital Image Computing: Techniques and Applications (DICTA 2010)

Type

Conference paper

DOI

10.1109/DICTA.2010.82

Restricted until

2037-12-31

Downloads

File

Description

01_Qian_L1/2_Sparsity_Constrained_2010.pdf (168.83 KB)