Abstract
In hyperspectral image analysis an exploratory approach to analyse
the image data is to conduct subspace projections.
As linear projections often fail to capture the underlying structure of
the data, we present kernel based subspace projections of PCA and
Maximum Autocorrelation Factors (MAF). The MAF projection
exploits the fact that interesting phenomena in images typically
exhibit spatial autocorrelation.
The analysis is based on nearinfrared
hyperspectral images
of maize grains demonstrating
the superiority of the kernelbased
MAF method.
| Original language | English |
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| Publication date | 2009 |
| Publication status | Published - 2009 |
| Event | European Workshop on Challenges in Modern Massive Data Sets - Kgs. Lyngby, Denmark Duration: 1 Jan 2009 → … |
Conference
| Conference | European Workshop on Challenges in Modern Massive Data Sets |
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| City | Kgs. Lyngby, Denmark |
| Period | 01/01/2009 → … |