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This paper presents spectral characterization for efficient image detection using hyperspectral processing techniques.
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Interchangeable optical filters were selected to optimise the contrast between the food residues and processing surfaces, using hyperspectral fluorescence imaging.
The pixel wise visualization of WHC obtained with the aid of image processing was another advantage of using hyperspectral imaging that cannot be obtained with either imaging or conventional spectroscopy.
Regression models were used to estimate LAI using hyperspectral reflectance data from the Pushbroom Hyperspectral Imager (PHI) and in-situ measurements.
By using hyperspectral two-photon microscopy, we showed that 3D morphology can not only be visualized, but that different structures have unique spectral signatures.
As a test case, interaction zones between the coral Pocillopora sp. and a red alga Gracilaria sp. were characterized in detail using hyperspectral imaging.
Additionally, improved localization of inclusions was observed for both sets when using hyperspectral information.
Although exact values of concentrations were not achieved there is a notable improvement shown when using hyperspectral information.
Using hyperspectral imaging to collect DRS enables flexible selection of SDSs which can be optimized for minimizing differences between the input data and fitted spectra.
A comparison of Tests 1 and 2 allowed us to estimate the improvement conferred by using primitive hyperspectral processing, i.e., simply taking the average of all the bands.
The system uses hyperspectral images and height information and includes the stages (1) preprocessing, (2) hyperspectral surface material mapping, (3) indicator calculation and (4) multi-level validation.
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