In this section you are invited to read about some terms that might be interesting to know for the implementation of hyperspectral technology in industrial applications. This section will be continuously extended.
What is Chemical Colour Imaging?
What is Hyperspectral Imaging?
Hyperspectral imaging, like other spectral imaging, collects and processes information from across the electromagnetic spectrum. Much as the human eye sees visible light in three bands (red, green, and blue), spectral imaging divides the spectrum into many more bands. This technique of dividing images into bands can be extended beyond the visible.
Engineers build sensors and processing systems to provide such capability for application in agriculture, mineralogy, physics and surveillance. Hyperspectral sensors look at objects using a vast portion of the electromagnetic spectrum. Certain objects leave unique ‘fingerprints’ across the electromagnetic spectrum. These ‘fingerprints’ are known as spectral signatures and enable identification of the materials that make up a scanned object. For example, a spectral signature for oil helps mineralogists find new oil fields.
This enhanced ability increases the probability of detecting materials of interest and provides additional information necessary for identifying and classifying these materials.
Hyperspectral cubes are generated by hyperspectral imaging sensors. Hyperspectral sensors collect information as a set of ‘images’. Each image represents a range of the electromagnetic spectrum and is also known as a spectral band. These ‘images’ are then combined and form a three-dimensional hyperspectral data cube for processing and analysis.
The precision of these sensors is typically measured in spectral resolution, which is the width of each band of the spectrum that is captured. If the scanner detects a large number of fairly narrow frequency bands, it is possible to identify objects even if they are only captured in a handful of pixels. However, spatial resolution is a factor in addition to spectral resolution. If the pixels are too large, then multiple objects are captured in the same pixel and become difficult to identify. If the pixels are too small, then the energy captured by each sensor cell is low, and the decreased signal-to-noise ratio reduces the reliability of measured features.