RT Journal Article
A1 G. Camps-Valls
AD Image Processing Laboratory (IPL), University of València, València, Spain

PB iet
T1 Kernel spectral angle mapper
JN Electronics Letters
VO 52
IS 14
SP 1218
OP 1220
AB This communication introduces a very simple generalisation of the familiar spectral angle mapper (SAM) distance. SAM is perhaps the most widely used distance in chemometrics, hyperspectral imaging, and remote sensing applications. It is shown that a nonlinear version of SAM can be readily obtained by measuring the angle between pairs of vectors in a reproducing kernel Hilbert spaces. The kernel SAM generalises the angle measure to higher-order statistics, it is a valid reproducing kernel, it is universal, and it has consistent geometrical properties that permit deriving a metric easily. We illustrate its performance in a target detection problem using very high resolution imagery. Excellent results and insensitivity to parameter tuning over competing methods make it a valuable choice for many applications.
K1 SAM nonlinear version
K1 kernel spectral angle mapper
K1 chemometrics
K1 angle measurement
K1 spectral angle mapper SAM distance
K1 remote sensing application
K1 high-resolution imagery
K1 kernel SAM
K1 SAM distance generalisation
K1 higher-order statistics
K1 parameter tuning
K1 kernel Hilbert spaces
K1 target detection problem
K1 geometrical properties
K1 hyperspectral imaging
DO https://doi.org/10.1049/el.2016.0661
UL https://digital-library.theiet.org/;jsessionid=20pusecnomaml.x-iet-live-01content/journals/10.1049/el.2016.0661
LA English
SN 0013-5194
YR 2016
OL EN