subspace anomaly detector
PT B
AU Nasrabadi, NM
AF Nasrabadi, Nasser M.
GP IEEE
TI KERNEL SUBSPACE-BASED ANOMALY DETECTION FOR HYPERSPECTRAL IMAGERY
SO 2009 FIRST WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING:
EVOLUTION IN REMOTE SENSING
LA English
DT Proceedings Paper
CT 1st Workshop on Hyperspectral Image and Signal Processing - Evolution in
Remote Sensing
CY AUG 26-29, 2009
CL Grenoble, FRANCE
SP IEEE Geosci & Remote Sensing Soc
DE Anomaly detection; kernel machine learning
ID EIGENSPACE SEPARATION TRANSFORM
AB This paper provides a performance comparison of various linear and nonlinear subspace-based anomaly detectors. Three different techniques, Principal Component Analysis (PCA), Fisher Linear Discriminant (FLD) Analysis, and the Eigenspace Separation Transform (EST), are used to generate the linear projection subspaces. Each of these three linear methods is then extended to its corresponding nonlinear kernel version. The well-known Reed-Xiaoli (RX) anomaly detector and its kernel version (kernel RX) are also implemented. Comparisons between all linear and non-linear anomaly detectors are made using receiver operating characteristics (ROC) curves for several hyperspectral imagery.
C1 USA, Res Lab, Adelphi, MD 20783 USA.
RP Nasrabadi, NM (reprint author), USA, Res Lab, 2800 Powder Mill Rd, Adelphi, MD 20783 USA.
NR 6
TC 0
Z9 0
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4244-4686-5
PY 2009
BP 83
EP 86
PG 4
WC Engineering, Electrical & Electronic; Remote Sensing
SC Engineering; Remote Sensing
GA BOC66
UT WOS:000276190400021
ER
