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

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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


In 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.
Original languageEnglish
Title of host publicationProceedings on the 16th Scandinavian Conference on Image Analysis
EditorsArnt-Børre Salberg
Place of PublicationNew York
Publication date2009
Publication statusPublished - 2009
Event16th Scandivanian Conference on Image Analysis - Oslo, Norway
Duration: 15 Jun 200918 Jun 2009
Conference number: 16


Conference16th Scandivanian Conference on Image Analysis
SeriesLecture Notes in Computer Science


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