Kernel based subspace projection of near infrared hyperspectral images of maize kernels

Rasmus Larsen, Morten Arngren, Per Waaben Hansen, Allan Aasbjerg Nielsen

AbstractIn this paper we present an exploratory analysis of hyper-
spectral 900-1700 nm images of maize kernels. The imaging device is
a line scanning hyper spectral camera using a broadband NIR illumi-
nation. In order to explore the hyperspectral data we compare a series
of subspace projection methods including principal component analysis
and maximum autocorrelation factor analysis. The latter utilizes the fact
that interesting phenomena in images exhibit spatial autocorrelation.
However, linear projections often fail to grasp the underlying variability
on the data. Therefore we propose to use so-called kernel version of the
two afore-mentioned methods. The kernel methods implicitly transform
the data to a higher dimensional space using non-linear transformations
while retaining the computational complexity. Analysis on our data ex-
ample illustrates that the proposed kernel maximum autocorrelation fac-
tor transform outperform the linear methods as well as kernel principal
components in producing interesting projections of the data.
KeywordsKernel methods, maximum autocorrelation factor, principal components
TypeConference paper [With referee]
ConferenceProceesings of the 16th Scandinavian Conference on Image Analysis
Year2009    Vol. 5575    pp. 560-569
SeriesLecture Notes in Computer Science
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics