Visualization of spectral images

Spectral image sensors provide images with a large number of contiguous spectral channels per pixel. Visualization of these huge data sets is not a straightforward issue. There are three principal ways in which spectral data can be presented; as spectra, as image and in feature space. This paper describes several visualization methods and their suitability in the different steps in the research cycle. Combinations of the three presentation methods and dynamic interaction between them, adds significant to the usability. Examples of some software implementations are given. Also the application of volume visualization methods to display spectral images is shown to be valuabl

Spectral image analysis for measuring ripeness of tomatoes

In this study, spectral images of five ripeness stages of tomatoes have been recorded and analyzed. The electromagnetic spectrum between 396 and 736 nm was recorded in 257 bands (every 1.3 nm). Results show that spectral images offer more discriminating power than standard RGB images for measuring ripeness stages of tomatoes. The classification error of individual pixels was reduced from 51% to 19%. Using a gray reference, the reflectance can be made invariant to the light source and even object geometry, which makes it possible to have comparable classification results over a large range of illumination conditions. Experimental results show that, although the error rate increases from 19% to 35% when using different light sources, it is still considerably below the 51% for RGB under a single light sourc

The VTTVIS line imaging spectrometer - principles, error sources, and calibration

Hyperspectral imaging with a spatial resolution of a few mm 2 has proved to have a great potential within crop and weed classification and also within nutrient diagnostics. A commonly used hyperspectral imaging system is based on the Prism-Grating-Prism (PGP) principles produced by Specim Ltd. Finland. One of the novel systems based on the PGP spectrograph (VTTVIS) was build by The Department of Agricultural Sciences, AgroTechnology, KVL, Denmark, in 1995. Several other agricultural institutions have now implemented the technology in their research. None of these has published any thoroughly work describing the basic principles, potential error sources, and/or adjustment and calibration procedures. This report fulfils the need for such documentation with special focus on the system at KVL. The PGP based system has several severe error sources, which should be removed prior to any analysis. Most of the random noise sources can be minimised by carefully selecting high-grade components especially wit