Overview hyperspectral image analysis
The technique developed and introduced here is also applicable for the analysis of hyperspectral images. Again, the complete information of one spectrum is used within the fitted model, but for each pixel this time. Contrary to classification with respect to the experimental design with known classes the situation varies within the image situation, as all continuous steps of one factor are somehow depicted here. Consequently we use a cluster analysis instead of the discriminant analysis. The cluster analysis does not require a beforehand classification. The downy mildew, (Plasmopara viticola)) experiment is chosen again as an example1. Parallel to infected leaf measurements with the plant probe, hyperspectral images have been taken by a line scan camera. The RGB image (right) depicts the upper side of a leaf. It tests the question how pathogen related pattern of the lower side of the leaf are detectable on the top side based on hyperspectral information. To test the hypothesis the problem is partitioned in several steps after model fitting.Part A: Imaging model parameter (arbitrary coloring in 8 classes)
The parameters describing the relative position on the y-axis, image the origin very well (left), other parameters are important by its physiological meaning(center), and further parameters of the model show a weak conformity with the RGB image.
Application in field experimentsTo test the developed index we used a camera image of the company Cubert 2 taken from a sugar beet field of the JKI Elsdorf. The scientific background was to test the ability of density estimation of the sugar beet nematode based on the hyperspectral information of the sugar beet canopy. The data were part of the a Nemaplot Kai Schmidt initiated and EU-funded project and were taken at the JKI Elsdorf. The report (German only) can be downloaded from the link on the right. The report explains the background of typical, nematode related spectra for the nematode problem. To explain the picture below, the population density of nematodes is usually estimated from extremely laborious soil samples, means, the spatial distribution in field situations of the worm is known, on smaller scales the density is based on one or two soil samples and nothing is known about the distribution.
|RGB transformed hyperspectral image of a sugar beet plot with an extremely high population density of about 4000 eggs and larvae / 100 ml soil determined by soil samples. The plot is framed by pathways, the plot boundaries are marked by white standard markers. Picture is taken with a Cubert2 camera, the usable wavelength ranges from 400 to 900 nm.||Octocopter based Cubert2- camera with a resolution of 50 x 50 pixel.|
Conclusion: The combination of aerial information and the confidence areas of the spectra allow some kind of conclusion about the underlying nematode population, or, more theoretically, it links the more abstract clusters more or less with the scientific hypothesis of the experiment.