Graph-Regularized Fast and Robust Principal Component Analysis for Hyperspectral Band Selection

A fast and robust principal component analysis on Laplacian graph (FRPCALG) method is proposed to select bands of hyperspectral imagery (HSI). The FRPCALG assumes that a clean band matrix lies in a unified manifold subspace with low-rank and clustering properties, whereas sparse noise does not lie i...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 56; H. 6; S. 3185 - 3195
Hauptverfasser:	Sun, Weiwei, Du, Qian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Analysis Approximation Band selection Banded structure Centroids Classification Clustering Clustering algorithms Columns (structural) Computational efficiency Computer applications hyperspectral imagery (HIS) Hyperspectral imaging Imagery Laplace equations Laplacian graph (LG) Manifolds Manifolds (mathematics) Matrix decomposition Principal components analysis Regularization robust principal component analysis (RPCA) Robustness Set theory Sparse matrices structured random projection (SRP) Subspace methods Subspaces
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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